Intelligent maintenance for container terminal equipment towards Industry 4.0

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This report consists of 60 pages and 0 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.

Specialization: Transport Engineering and Logistics Report number: 2017.TEL.8175

Title: Intelligent maintenance for container terminal equipment towards Industry 4.0

Author: Mark Houweling

Assignment: Literature Assignment Confidential: No

Supervisor: dr.ir. Y. Pang

Date: December 6, 2017

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Subject: Intelligent maintenance for container terminal equipment towards Industry 4.0 Maintenance of equipment is a significant part of the total operating costs in most industry sectors. Smart technologies can help improve maintenance management and executing of maintenance. Today we are at the eve of wat is called ‘Industry 4.0’ Industry 4.0 is a name for the current trend of

automation and data exchange in manufacturing technologies.

Container terminals have an ever-increasing pressure from different actors to be more efficient and sustainable. The newest terminals are almost fully automated. When equipment is planned to act in an automated system they strive for zero breakdown of equipment and safe environments. Intelligent maintenance could help this. This report should contain the following:

 How can maintenance be categorized  What does intelligent maintenance mean

 How can intelligent maintenance be implemented for container terminal equipment

 What does Industry 4.0 mean and how can intelligent maintenance benefit form Industry 4.0  How do container terminals operate and what kind of equipment is used

 What are the current developments in maintenance for container terminal equipment

 What are the opportunities and challenges for the future of maintenance of container terminal equipment

The report should comply with the guidelines of the section. Details can be found on the website.

The mentor,

Student: M.A. Houweling Assignment type: Literature Supervisor (TUD): dr. ir. Y. Pang Creditpoints (EC): 10

Specialization: TEL

Report number: 2017.TL.8175 Confidential: no

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Summary

Four types of maintenance can be distinguished: preventive maintenance, random maintenance, corrective maintenance and predictive maintenance. Predictive maintenance is the desired form since this strives to zero downtime for equipment. Managing this predictive process effectively without computer-based support is almost impossible and that is where a well implemented computerized maintenance management comes in to underpin proactive maintenance management. When

management systems become intelligent, intelligent maintenance or e-maintenance is introduced. This introduces the following about the system to be maintained.

 Base knowledge, understanding the physical system to be maintained and its critical features and characteristics from which its performance and health can be predicted.

 Data acquisition system: an ICT system for monitoring the physical system to be maintained and collecting relevant data about equipment features and characteristics with the capability to share it over the intranet, extranet, and the Internet.

 Mathematical and statistical models: these are models to support maintenance decision making and that have the capability to estimate physical system reliability, remaining useful life, determining when maintenance action is needed, and plan and schedule the

maintenance.

 Performance reporting: this is a system for reporting performance including e-maintenance, production, and other services.

Current developments in technology have led to what is called Industry 4.0 and introduces terms as Internet of thing (IoT), Big data, Cyber-physical systems (CPS) and Augment Reality (AR) lead to the ability to get more data from a system to be analyzed which means a better prediction for

maintenance. With AR the manually executed maintenance can be better and faster executed. Container terminals have an ever-increasing pressure from different actors to be more efficient and sustainable. Container terminal equipment can be distinguished in 3 regimes:

 Ship to shore crane

 Horizontal vehicle transport  Stack handling equipment

More and more automation come in to container terminals, especially in the horizontal vehicle transport and the stack handling equipment.

Container terminals use terminal operation systems to control movements and actions that need to be taken.

Terminals use maintenance management software (MMS) and is used to track maintenance

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do maintenance. The MMS system is getting more and more intelligent in terminals. This done by condition monitoring the equipment on real time basis.

When looking in the future Self-maintenance Systems and Engineering immune systems come in. Which use automated monitoring (sensors) of most components (integration), followed by automated decision-making (intelligence) and automated maintenance activities (robots). Cybersecurity will be a challenge to deal with in the world of Industry 4.0.

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1. Introduction

In the second year of the master Mechanical Engineering, track Transport Engineering and Logistics (TEL), a literature assignment has to be performed. The objective of the literature assignment is to acquire specific knowledge on a certain subject of choice. This information has to be gained by searching information in for example books, scientific articles, journals, and brochures. The literature assignment contains a clear and logical stating of the found information and own interpretations and findings of the student [1].

Maintenance of equipment is a significant part of the total operating costs in most industry sectors. Smart technologies can help improve maintenance management and executing of maintenance. Today we are at the eve of wat is called ‘Industry 4.0’ Industry 4.0 is a name for the current trend of

automation and data exchange in manufacturing technologies.

Container terminals have an ever-increasing pressure from different actors to be more efficient and sustainable. The newest terminals are almost fully automated. When equipment is planned to act in an automated system they strive for zero breakdown of equipment and safe environments. Intelligent maintenance could help this.

This literature assignment has the title:

- Intelligent maintenance for container terminal equipment towards industry 4.0.

In this title certain subject come forward, on these subjects some questions that focus on the title will be answered in this report. The questions are stated below the subjects:

- Intelligent maintenance

 How can maintenance be categorized?

 What does intelligent maintenance mean and how can it be or is it implemented for container terminal equipment?

- Industry 4.0

 What does industry 4.0 means and how can intelligent maintenance benefit form industry 4.0?

- Container terminal and equipment

 How do container terminals operate and what kind of equipment is used?

 What are the current developments in maintenance for container terminal equipment?  What are the opportunities and challenges for the future of maintenance of container

terminal equipment?

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2. Maintenance

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2. Maintenance

In the field of maintenance, most product maintenance today is either purely reactive (fixing or replacing equipment after it fails) or blindly proactive (assuming a certain level of performance degradation, with no input from the machinery itself, and servicing equipment on a routine schedule whether service is actually needed or not). Both scenarios are extremely wasteful and result in costly production or service downtimes [2]. To get a better insight in how maintenance is categorized, types of maintenance are catechized according to N. Jimenze-Redondo [3] and G. Lodewijks [4] in section 2.1.

In today’s competitive market, production costs, lead time and optimal machine utilization are crucial values for companies. Since machine or process breakdowns severely limit their effectiveness, methods are needed to predict products’ life expectancy [2].

To get insight in maintenance, management systems are helpful and will be discussed in section 2.2. To better predict when maintenance is needed, intelligent maintenance systems or e-Maintenance come in sight and are discussed in section 2.3.

2.1 Categorizing Maintenance

In general there are many types of classifications for maintenance tasks, according to the type of activity that needs to be performed on the object and the type of intervention with the process the object is working in. The four typical types of maintenance and their type of intervention are discussed below according to G. Lodewijks [4].

2.1.1 Preventive maintenance

Preventive maintenance tends to follow planned guidelines from time-to-time to prevent equipment and machinery breakdown, so the activities are planned depending on i.e. working hours or at certain time intervals (scheduled maintenance). The work is carried out on equipment in order to avoid its breakdown or malfunction. And consists of maintenance, including tests, measurements, adjustments, parts replacement, and cleaning, performed specifically to prevent faults from occurring. A typical example of preventive maintenance is oil change after several running hours of a machine.

2.1.2 Random maintenance

Random maintenance is opportunity based, i.e. maintenance is done when the opportunity arises; the decision to maintain a component based on opportunities may or may not be triggered by the

condition of a component.

2.1.3 Corrective maintenance

Corrective maintenance is emergency based, i.e. repairing when a component malfunctions, this may cause a general shutdown of the system, the repair activity was not scheduled beforehand. Often this is a very cost full because this is not planned.

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2.1.4 Predictive maintenance (PdM)

Pdm is condition based, i.e. components are being monitored and when irregular factors are discovered, one waits until a maintenance opportunity arises, it is a planned and corrective maintenance. Because predictive maintenance tends to intelligent maintenance, PdM is further discussed. The book written by A. Raouf about planning and control of maintenance systems [5] describes PdM in a broad way:

PdM is a proactive type of maintenance that predicts failure or degradation state where maintenance action is necessary to minimize downtime and outage costs. PdM utilizes techniques such as vibration analysis, infrared thermography, tribology (oil analysis), noise and temperature to continuously monitor equipment degradation, and its evolution to predict failure. The maintenance action in PdM is based on the health condition of the equipment or the system and that is the reason it is known in the literature as condition-based maintenance (CBM). PdM has three main phases:

 Surveillance—monitoring the equipment condition and collecting data that are relevant and can be used effectively to detect developing problems using various statistical and

mathematical techniques.

 Diagnosis and prediction—isolating the root cause of the problem and developing a

maintenance action plan based on priority, equipment state of degradation, risks of failure, and its remaining useful life.

 Correct and prevent—Identify and perform corrective and preventive actions based on the outcome of step 2 above.

P–F Interval

The logic of PdM depends on the fact that most failures do not occur instantaneously, but in fact, equipment goes through a measurable process of degradation until they fail. Actually, there is a point in time where failure starts (not related to age and perhaps not detectable by existing technologies); however, there is a point P where humans can detect that failure has started and will take an interval till functional failure happens. This interval is known as the P–F interval, and W. Waller [6] refer to it as delay time. Figure 1 illustrates the process of failure and shows the P–F interval.

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Figure 1 P-F interval [6].

Although many failure modes are not age-related, most of them give some sort of warning that they are in the process of occurring or about to occur. The frequency of predictive maintenance tasks has nothing to do with the frequency of failure and nothing to do with the criticality of the item. It is often possible to detect the fact that the failure is occurring during the final stages of deterioration, it may be possible to take action to prevent it from failing completely and/or to avoid the consequences. In practice, there are many ways of determining whether failures are in the process of occurring (e.g., hot spots showing deterioration of furnace refractories or electrical insulation, vibrations indicating imminent bearing failure, increasing level of contaminants in lubricating oil).

The P–F interval governs the frequency with which the predictive task must be done. The checking interval must be significantly less than the P–F interval if we wish to detect the potential failure before it becomes a functional failure. The P–F interval can be measured in any units relating to exposure to stress (running time, units of output, stop–start cycles, etc.), but it is most often measured in terms of elapsed time. The amount of time needed to respond to any potential failures which are discovered also influences condition-based task intervals.

The main idea behind PdM is to use the equipment or product degradation information to minimize downtime by ensuring that maintenance is taken at the right time, when it is needed and thereby avoiding under- and over maintaining. Many techniques have been developed to help in features extraction, predicting failure or departure from acceptable performance. The techniques include signal processing technologies, neural networks, fuzzy logic, and decision trees.

2.2 Management systems

According to M. Wienker [7], maintenance of equipment is a significant part of the total operating costs in most industry sectors, but its real impact is often under-estimated. The “Iceberg Model” (see

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Figure 2) highlights the hidden cost impact of maintenance upon the business which is much greater than just the direct costs associated with traditional maintenance.

Figure 2 explains that there are a lot of hidden costs in the field of maintenance that are not easily detected or seen by the management (iceberg under sea level), other than maintenance costs that are easily to foresee e.g. labor for scheduled maintenance (iceberg above sea level). An example of indirect hidden maintenance costs is when a bearing of a machine fails, which can be hard to detect, more friction has to be overcome by the machine and therefore the systems uses more energy. This could also lead to safety risks, environmental issues, wasted resources, reduced asset life, lower quality of the product that is made by the machine, lost production due to e.g. late delivery of the new bearing. This can be solved by replacing the bearing preventively, but this can lead to over maintaining the machine.

Figure 2 Iceberg model [7].

According to M. Wienker many companies, reducing these hidden costs requires a shift from the traditional reactive approach (“fix it when it breaks”) to a proactive reliability-based approach. For such a shift to be sustainable, a number of key elements must be put in place including:

 A clear strategy

 Policies to support the strategy

 Procedures & processes to enable implementation of the strategy & policy  Tools to support this implementation

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These key elements form the basis of “maintenance management”. Such maintenance management is a complex process requiring an effective combination of technical and economic expertise. One part of maintenance management is to interpret the data available and turn it into useful information in order to manage the equipment in the best possible way. To do so, the data must be gathered and analyzed in a structured manner otherwise it cannot be effectively utilized. Managing this process effectively without computer-based support is almost impossible and that is where a well implemented computerized maintenance management (CMMS) or electronic asset management (eAM) system is one of the key tools that is essential to underpin proactive maintenance management [7].

2.3 Maintenance towards intelligence and automation

According to Y. Pang [8], 5 steps can be distinguished in human controlled or automated controlled maintenance. This helps indicating where maintenance systems are standing towards intelligence and automation.

 Step 1: Visual observation and inspection of critical components, followed by human decision-making and manual maintenance activities.

 Step 2: Automated monitoring (sensors) of critical components, followed by human decision-making and manual maintenance activities.

 Step 3: Automated monitoring (sensors) of most components (integration), followed by human decision-making and manual maintenance activities.

 Step 4: Automated monitoring (sensors) of most components (integration), followed by automated decision-making (intelligence) and manual maintenance activities.

 Step 5: Automated monitoring (sensors) of most components (integration), followed by automated decision-making (intelligence) and automated maintenance activities (robots).

2.4 Intelligent maintenance systems and e-Maintenance

As stated in section 2.2, companies with maintenance management systems can have better insight and track of their equipment and how it is maintained. This kind of management is situated in Step 3 from section 2.3. When having insight in maintenance data, smart analyses can be done with this, when having the right data. Here intelligent maintenance systems and e-maintenance are terms that come by. This goes to Step 4 as stated in section 2.3.

According to the book written by A. Raouf [5] e-maintenance and intelligent maintenance systems, are a relatively new approach to maintain engineering and management that was coined by J. Lee around 2000. An intelligent maintenance system (IMS) is a system that utilizes the collected data from equipment and machinery in order to predict and prevent the potential failures.

2.4.1 Intelligent Maintenance

The IMS center focuses on intelligent maintenance systems. This is their vision according their website [9]: The IMS center is internationally recognized as the leader in predictive analytics and industrial big

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data modeling for life cycle performance of industrial systems. As the world pre-eminent NSF

Industry/University Cooperative Research Center, IMS is often the first to introduce new concepts and technologies to the research and industry communities. It is also a leader in the discovery of new methods to assess machine degradation and predict the health of industrial systems including e-manufacturing, e-maintenance, cyber machine systems, cloud-based machine monitoring and

manufacturing, intelligent cyber machine systems, etc. (These terms are related to industry 4.0 which will be discussed in section 3.)

These discoveries translate into a useful methodology and tools for the development and deployment of prognostics and health management of industrial systems. The Center envisions the future of maintenance as a system that enables equipment to achieve and sustain near-zero breakdown performance with maintenance capabilities (aware, predict, compare, and self-configure), and ultimately to realize the autonomous transformation of raw data to useful information for improved reliability, productivity, and asset utilization. IMS is focused on frontier technologies in predictive analytics including prognostics and health management technologies, cyber physical

systems, industrial big data analytics, and intelligent decision support tools. The center has coined the trademarked Watchdog Agent® prognostics tools and Device-to-Business (D2B) predictronics platform for e-maintenance applications, which are schematically shown in Figure 3.

Figure 3 Schematic structure of IMS [9]. 2.4.1.1 Watchdog agent

In the papers of D. Djrudjanovic and J. Lee [2], the Watchdog agent is briefly discussed: The Watchdog Agent™ bases its degradation assessment on the readings from multiple sensors that measure critical properties of the process, or machinery that is being considered. It is expected that

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the degradation process will alter the sensor readings that are being fed into the Watchdog Agent™, and thus enable it to assess and quantify the degradation through quantitatively describing the corresponding change of sensor signatures. In addition, a model of the process or piece of equipment that is being considered, available application specific knowledge, or prior historical records of

equipment behavior can be used to aid the degradation process description, provided that such a model, expert knowledge or historical records exist. The prognostic function of the Watchdog is realized through trending and modeling of the dynamics of the observed process performance signatures and/or model parameters. This allows one to predict the future behavior of these patterns and thus forecast the behavior of the process, or piece of machinery that is being considered. Furthermore, the Watchdog Agent™ also has the diagnostic capabilities through memorizing the significant signature patterns in order to recognize situations that have been observed in the past, or be aware of the situation that was never observed before. Thus, the Watchdog Agent™ has elements of intelligent behavior that enable it to answer the questions:

 When the observed process, or equipment is going to fail, or degrade to the point when its performance becomes unacceptable.

 Why the performance of the observed process, or equipment is degrading, or in other words, what is the cause of the observed process or machinery degradation.

The answer to the first question enables the prognostic Watchdog function and the answer to the second question enables its diagnostic function. Thus, in essence, the functionality of the Watchdog Agent can be summarized in the following three tasks:

 Quantitative multi-sensor assessment of performance degradation.  Forecasting of performance degradation.

 Diagnosis of the reasons of the current or predicted performance degradation.

The prognostic and diagnostic outputs of Watchdogs mounted on all the processes and machinery of interest can then be fed into a decision support tool (DST) that addresses the question: What is the most critical object, or process in the system with respect to maintenance, or repair.

The answer to this question is obtained through taking into account the risks of taking, or not taking the maintenance action at a given time, and then optimizing the costs associated with the

maintenance operation if the decision to perform maintenance is made, or the cost of downtime and repair if the maintenance is omitted and the process or machine fails.

Thus, the output of the DST module is an optimal maintenance policy for a number of objects in the system. Those objects are traditionally processes and/or equipment, and the system could be a manufacturing line, or a plant. However, there is no reason why an object could not be a hardware, or software component of a vehicle, and the associated system can be the vehicle itself, or the

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Therefore, the operation of an IMS can be summarized in answering the previously described ‘when’, ‘why’ and ‘what’ questions in order to postulate an optimal maintenance/repair set of decisions that facilitate an optimal set of maintenance/repair actions that enable near zero downtime of the production/service system and maximizes the cost benefits of the predictive machine-level

information. Such a system of Watchdogs integrated by a DST thus enables maintenance that is in the same time condition based, as well as predictive and proactive.

Furthermore, as indicated in [2] information about current and predicted performance degradation of components in a product is indispensable in assessing remaining life of those components and possibility of their cost-effective and safe disassembly and reuse in other products. Namely, demands regarding product production, use and disposal continue to increase because of population growth and amplified product expectations, and fulfilling those increasing demands while preserving the natural environment and resources can be realized only through a significant reduction of energy and resource consumption, accompanied by a dramatic increase in efficiency of energy and resource usage. Quantitative description and prediction of performance degradation can be utilized to identify those products or components with low-levels of degradation and substantial useful remaining life so that they could be efficiently and cost-effectively disassembled and reused in another system [2]. Section 4.2.1.1 is visualized in Figure 4.

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2.4.2 E-maintenance

According A. Raouf [5] E-maintenance is the backbone of i-maintenance. Today, e-maintenance became a very common term in the maintenance literature and many companies such as Canon, American Petroleum Institute (API), British Petroleum (BP), and American Telephone & Telegraph (AT&T) is advertising on the Internet e-maintenance programs for their products. The following factors have contributed to the emergence of e-maintenance:

 The recognition of the role and impact of maintenance on business in a highly competitive market place.

 The need for high performance, near 100 % availability and zero product defects.  The impact of maintenance on safety and the environment.

 The advancement in information and communication technologies (ICT).

According to L. Benoit [10] at present we can find different complementary definitions of the term e-Maintenance. These definitions apply to maintenance several principles and concepts such as

collaboration, pro-activity, knowledge, intelligence, web services or the Internet. A clear consensus is not yet reached, even when some contributions, discussed in this paper, try to propose unique repositories to ensure consistency.

A. Raouf [5] believes that condition-based maintenance is the root of e-maintenance and still shares several features that include predicting, preventing, and performing maintenance effectively and efficiently. ICT has enabled e-maintenance to integrate network of machines, and technology has enabled e-maintenance to share data effectively. This has added to the visibility of equipment health and performance. This visibility has made it easier to react and take maintenance tasks as needed at the right time. The elements of e-maintenance include the following:

 Base knowledge: understanding the physical system to be maintained and its critical features and characteristics from which its performance and health can be predicted.

 Data acquisition system: an ICT system for monitoring the physical system to be maintained and collecting relevant data about equipment features and characteristics with the capability to share it over the intranet, extranet, and the Internet.

 Mathematical and statistical models: these are models to support maintenance decision making and that have the capability to estimate physical system reliability, remaining useful life, determining when maintenance action is needed, and plan and schedule the

maintenance.

 Performance reporting: this is a system for reporting performance including e-maintenance, production, and other services.

Intelligent prognostics is defined by J. Lee [11] as a systematic approach that can continuously track health degradation and extrapolate temporal behavior of health indicators to predict risks of

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to fail. Intelligent prognostics coupled with information flow in which maintenance actions are synchronized with the operation of the system as well as the maintenance resources has enabled maintenance experts to move from PdM to e-maintenance. The synchronization of maintenance actions and information flow infrastructure enhances visibility and enables autonomous triggering of services and ordering of parts. Such an integrated system with a high visibility is expected to result in near 100 % availability. The key elements of e-maintenance are the following:

 Micro-electromechanical and wireless sensors,

 Web-based and semantic maintenance technologies and tools,  Mobile devices,

 Asset self-identification technologies, such as RFID, and

 Computing facilities equipped with statistical and mathematical models for data processing and analysis.

e-maintenance employs the above technologies, know-how, and software to integrate maintenance stakeholders, tools, processes, and data. This integration makes equipment conditions and decisions to be made visible to maintain technical staff that is enabled to take cost-effective maintenance actions when needed and at the right time. The elements of e-maintenance mentioned above provide services such as maintenance documentation, predictive health monitoring and maintenance planning services, performance assessment integration, as well as training and knowledge management at the machine level under dynamic conditions. Their range includes services to deliver anywhere, any time, and to anyone authorized to have access.

According to E. Jantunen [12] we have seen the emergence of e-Maintenance techniques in the last decade. The main enabling hardware elements of e-Maintenance are the RFID tags, the MEMS sensors together with the PDA. Together with the hardware development the extensive use of Internet and Web Service technology have laid down the new foundation for modern maintenance. Today the use of e-Maintenance is still at infant stage. However, the introduction of e-Maintenance is especially motivated by the changes of strategy of manufacturing industries towards the capability of providing services throughout the life time of the equipment they have manufactured. This step forward will be supported by the hardware improvement, but the most significant factor is the availability of new data that can support diagnosis and predictive health monitoring and support to raise them to a level that is of genuine benefit for the maintenance technicians. The progress of new signal analysis techniques joined with simulation models can be the factor that enable a revolution in the prediction of the life time of components of machinery. The technologies are obtainable today and their introduction could be easily justified economically but it will take some time before all the

available options will be taken into use. Naturally the success stories of those in the forefront will boost the adaption of new technologies in greater numbers.

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3. Industry 4.0

As stated in the end of section 2. New technologies boost E-Maintenance and intelligent maintenance. This leads to this new section about Industry 4.0.

According to N. Jazdi [13], the term Industry 4.0 was manifested for the first time at the Hannover Fair in 2012 with the presentation of the "Industry 4.0" initiative. Following the first Industrial Revolution "Mechanization" as a result of the invention of the steam engine, the second "Mass production" with the help of electricity and the third "Digitization" by the use of Electronics and IT, this marks the dawn of the fourth Industrial Revolution through the use of cyber physical systems (CPS) and the Internet of Things. The integration of cyber technologies that make the products Internet-enabled facilitates innovative services to achieve, among other things, Internet-based diagnostics, maintenance, operation, etc. in a cost-effective and efficient manner.

The term IoT will be discussed in section 3.1. When talking about inspection of equipment, according to G. Lodewijks [14], these days the developments are towards fully automated inspection systems. The IoT enables that more information from sensor systems becomes available that was not available in the past. All these sensor systems produce a vast amount of information. Big data implies a combination of databases too large and/or too diverse to maintain by regular database management systems. Big data will be discussed in section 3.2.

In his article about A Cyber-Physical Systems architecture for Industry, J. Lee [15] states that recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next

generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. Cyber physical systems will be discussed in section 3.3.

According to Inglobe Technologies [16] Augmented reality is one of the cutting-edge technologies involved in the industry 4.0 trend when talking about smart manufacturing, it is a technology which was seen just as a fancy toy until a few years back, but which has now reached the right level of maturity to be employed in a productive environment. Augmented reality will be discussed in section 3.4.

3.1 Internet of Things (IoT)

In literature the term the “Internet of Things” (IoT), which is multi-disciplinary, is defined in a number of different ways reflecting this multi-disciplinary nature [17], [18], [19], [20]. In [21] for example, the IoT is defined as “things or objects, which through addressing schemes interact with each other and cooperate with their neighbors to reach common goals”. In [22] the IoT are “interconnecting

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physical objects with computing and communication capabilities across a wide range of services and technologies”. Finally in [23] the IoT is perceived as “Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework…with Cloud computing as the unifying framework”. The first definition comes from a networking perspective, the second uses physical attributes as the base for the IoT definition and the third definition emphasizes the use of platforms and the cloud.

The IoT holds several disciplines and consists of multiple technologies. The technologies are

structured in such a way that they form a value chain between a SO and an end-user (see Figure 5), and they are:

 Data acquisition

 Identification and tracking  Communication and networking  Middleware

 Data storage and analytics  Applications

Figure 5 Value chain of IOT [14].

G. Atzori [21] identified three different definitions or visions on the IoT. The IoT can only be useful in application domains where these three visions intersect (see Figure 6) [23]. These visions are called:

 ‘Internet oriented’ vision  ‘Things oriented’ vision  ‘Semantic oriented’ vision

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Figure 6 IoT as a result of different visions [21]. The ‘Internet Oriented’ vision

The ‘Internet Oriented’ vision incorporates the requirement for a standardized communication architecture that allows the IoT to become widespread. Several researchers have studied this requirement and they all aim for the Internet Protocol (IP) to be used as the network technology for connecting objects in the IoT. According to Gershenfeld et al., the costs and complexity of setting up a global network out of heterogeneous local networks are currently too high and have to be

dramatically reduced. For this purpose the ‘Internet-0’ (I0) was introduced and devices, which are a part of it, embody seven principles [24]:

 Each I0 device uses IP as the connection standard, which removes the need for costly and complex translation interfaces.

 Communication protocol software is simplified and non-segregated.

 Each set of I0-deviced is able to work independently, thus there are no client/server relations.  An I0-device keeps track of its own identity.

 I0 uses bits, which are bigger than the network.

 Big bits allow data to be represented in the same way, no matter what medium conveys them.

 An I0-device will use open standards in order to make optimal use of available resources. These seven principles allow physical and virtual objects, given that they are equipped with the required technology, to seamlessly connect to a data network with communication, computation, sensing and storage functionalities. By allowing objects to share information in the same ‘language’ in

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any network, locally or globally, the system can be designed based on the required functionality rather than constrained by boundaries.

The ‘Things Oriented’ vision

The ‘Things Oriented’ vision sees the IoT as a network of smart physical or virtual objects with

extended Internet technologies and at the same time with a set of technologies that realizes this [21], [22]. The focus lies on the physical embodiment of the IoT. According to this vision the IoT concept has three characteristics, which apply to all the active objects in the network:

 Anything identifies itself: smart objects are identified with a unique digital name to establish relationships in that domain.

 Anything communicates: smart objects form ad hoc networks of interconnected objects.  Anything interacts: smart objects interact through sensing and actuation with their

environment.

Developing the solutions and technologies to realize these three characteristics is the basis of the ‘things oriented’ perspective. The ‘things oriented’ vision differs from known networking visions in that everything should be able to interact based on its own decision-making. Each single object is called a Smart Object (SO) and these SO’s have at least basic communication and computation functionalities. A SO is also uniquely identified with one name and address within the network and it may possess means to sense physical phenomena. This is the key feature, which distinguish SO’s from nodes normally used in networking systems. A system containing a large number of SO’s is seen as a dynamically distributed network. These SO’s produce and use information and are able to trigger actions that have an impact on the physical realm [22]. Challenges lie in how these functionalities and resources can be integrated in multiple services spanning the network, which should result in an “always responsive service” for the end-user. Key system-level features, as identified from the ‘things oriented’ perspective, are:

 Connected devices should be heterogeneous  Scalable addressing and information management

 Data exchange should be ubiquitous through proximity wireless technologies

 Energy-optimized solutions, minimizing the energy spent for communication and computation  Devices should be track- and traceable

 Device should be autonomous, the network should distribute intelligence  Data formats should be standardized

 Adequate security and privacy mechanisms The ‘Semantic Oriented’ vision

The ‘Semantic Oriented’ vision deals with representing, storing, interconnecting, searching and organizing information generated by the IoT [21]. It promotes the use of smart connectivity and context-aware computation features. These features should allow the technology to ‘disappear’ from

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the consciousness of the user. Raw sensor data obtained through data acquisition has no real value if there is no understanding of its context and meaning. The challenge lies in the translation of the collected data into information [25]. In the IoT an enormous amount of sensors will be connected to the Internet. It is however not feasible to process all the data that is being collected in real time, which ultimately leads to the generation of large and diverse databases or ‘Big Data’. Therefore, within the IoT a shared understanding of the situation of the users is required, as well as solid software and communication architectures and analytic tools that aim for autonomous and smart behavior [23].

3.2 Big data

Today, an overwhelming amount of data is generated and analyzed by enterprises, Social Media, Multimedia and the IoT [26]. Questions may arise however whether or not this data is useful. Individually it may be considered valueless, but when accumulated data is exploited, useful information can be identified, and potential forecasts can be made [27].

The subject of Big Data concerns the need for real-time analysis of enormous datasets and masses of unstructured data, which are gathered in various fields [28]. The data is numerous, it cannot be categorized within standard relational databases and the capturing- and processing processes are executed rapidly. The underlying engine of Big Data is supported by Cloud Computing. With Cloud Computing a much larger scale and more complex algorithms can be employed to meet the, continuously growing, demands of Big Data. However, the rapid evolution of Big Data left little time for the subject to mature in academic literature and there exist little consensus of the fundamental question when data is qualified as Big Data [29]. Still a substantial part of the found literature. Data the can be characterized by the following four V’s:

 Volume : the massive data volumes that are processed

 Variety : the data is collected from a great variety of sources in multiple formats  Velocity : data is acquired, sent and analyzed with high data transfer rates  Value : value is found in, first considered, unstructured and uncorrelated data The growth of Cloud Computing and Big Data further promote the growth of the IoT [30].

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The steps that are used in order to extract information from Big Data fall under the Big Data Value Chain (see Figure 7) and are as follows [30]:

 Data generation  Data preparation  Data storage  Data analysis  Data visualization Data generation

Data can come from anywhere, enterprises, social media, the IoT. The generated data from the IoT can be characterized as: (1) being immensely scaled since the IoT is deployed in a distributed manner, (2) having a great variety in data types, due to the variety in IoT devices, (3) being correlated in both space and time, and (4) having only a small portion of valuable data, due to potential noises in acquisition and transmission of the data.

Data preparation

Data preparation stands for all the necessary steps that need to be taken before data can be stored in the cloud. These steps are: (1) data collection, (2) data transmission and (3) data pre-processing. Data collection and -transmission in the IoT happens at component level and the different enabling technologies were introduced in the previous paragraph. The data which is sent by the SO ́s varies in consistency and the amount of noise. It is therefore considered a waste to store all this data in the cloud and pre-processing of the data is required. Various techniques and steps for pre-processing are proposed [30]:

 Integrate correlating data from multiple sources to provide a uniform view of the data  Cleanse data from inaccuracies and incompleteness by either deleting or modifying this data

set

 Eliminate redundancies via recognizing repetition or surplus of data Data storage

Prepared data is send towards a Data Storage center. Data storage systems for Big Data are classified as Direct Attached Storage (DAS) or Network Storage (NS). In DAS data is connected through

peripheral devices and is commonly used in small sized data storage centers. NS provides, via a network, ubiquitous data access via a union interface and is characterized for a strong expandability [28]. Most storage systems are limited in their computational- and/or storage capacity and should not be considered useful for Big Data. Instead Cloud Computing may be the right alternative.

Data analysis

The Data Analysis functionality refers to the information extracted from Big Data by applying complex algorithms and tools stated in [26], [30] and [29]. There exists a general consensus that multiple

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intelligent learning tools exist which can derive information from (un)structured data within numerical- and text files, web and mobile data, recorded audio, video and social media. If these learning tools do not show direct results, complex data mining models will be created which will further analyze the big data for new associations, behavior and classifications.

Data visualization

Data Visualization is the graphical representation of the learned knowledge via analysis in a more intuitive and effective way [30]. To have the necessary effect of the visualization, the information has to be conveyed graphically in both aesthetic- and functional form. Current known data visualizations are done separately, and these serve only their own purposes. The main challenge of creating a general solution lies in the immense size and dimension of Big Data.

3.3 Cyber Physical Systems (CPS)

According to J. Lee [15] Cyber-Physical Systems (CPS) is defined as transformative technologies for managing interconnected systems between its physical assets and computational capabilities [31]. With recent developments that have resulted in higher availability and affordability of sensors, data acquisition systems and computer networks, the competitive nature of today’s industry forces more factories to move toward implementing high-tech methodologies. Consequently, the ever-growing use of sensors and networked machines has resulted in the continuous generation of high volume data which is known as Big Data [32]. In such an environment, CPS can be further developed for managing Big Data and leveraging the interconnectivity of machines to reach the goal of intelligent, resilient and self-adaptable machines [33]. Furthermore, by integrating CPS with production, logistics and services in the current industrial practices, it would transform today’s factories into an Industry 4.0 factory with significant economic potential [34]. A brief comparison between current and Industry 4.0 factories is presented in Figure 8 [9]. Since CPS is in the initial stage of development, it is essential to clearly define the structure and methodology of CPS as guidelines for its implementation in industry. To meet such a demand, a unified system framework has been designed for general applications. Furthermore, corresponding algorithms and technologies at each system layer are also proposed to collaborate with the unified structure and realize the desired functionalities of the overall system for enhanced

equipment efficiency, reliability and product quality. CPS 5C level architecture

The proposed 5-level CPS structure, namely the 5C architecture, provides a step-by-step guideline for developing and deploying a CPS for manufacturing application. In general, a CPS consists of two main functional components: (1) the advanced connectivity that ensures real-time data acquisition from the physical world and information feedback from the cyber space; and (2) intelligent data management, analytics and computational capability that constructs the cyber space. However, such requirement is very abstract and not specific enough for implementation purpose in general. In contrast, the 5C architecture presented here clearly defines, through a sequential workflow manner, how to construct a CPS from the initial data acquisition, to analytics, to the final value creation. As illustrated in Figure 9,

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the detailed 5C architecture is outlined in the following text. In Figure 10 the applications and

techniques associated with each level of Figure 9 are shown and a visual interpretation is given. More specific and detailed information is discussed in the article of J. Lee [15].

Figure 8 Comparison of today's factory and Industry 4.0 factory [15].

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Figure 10 Applications and techniques associated with each level of the 5C architecture [15]. Smart connection

Acquiring accurate and reliable data from machines and their components is the first step in

developing a Cyber-Physical System application. The data might be directly measured by sensors or obtained from controller for enterprise manufacturing systems such as ERP. Two important factors at this level have to be considered. First, considering various types of data, a seamless and tether-free method to manage data acquisition procedure and transferring data to the central server is required where specific protocols such as MTConnect [35] and are effectively useful. On the other hand, selecting proper sensors (type and specification) is the second important consideration for the first level.

Data-to-information Conversion

Meaningful information has to be inferred from the data. Currently, there are several tools and methodologies available for the data to information conversion level. In recent years, extensive focus has been applied to develop these algorithms specifically for prognostics and health management applications. By calculating health value, estimated remaining useful life and etc., the second level of CPS architecture brings self-awareness to machines.

Cyber

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from every connected machine to form the machines network. Having massive information gathered, specific analytics have to be used to extract additional information that provide better insight over the status of individual machines among the fleet. These analytics provide machines with self-comparison ability, where the performance of a single machine can be compared with and rated among the fleet. On the other hand, similarities between machine performance and previous assets (historical

information) can be measured to predict the future behavior of the machinery. Cognition

Implementing CPS upon this level generates a thorough knowledge of the monitored system. Proper presentation of the acquired knowledge to expert users supports the correct decision to be taken. Since comparative information as well as individual machine status is available, decision on priority of tasks to optimize the maintaining process can be made. For this level, proper info-graphics are necessary to completely transfer acquired knowledge to the users.

Configuration

The configuration level is the feedback from cyber space to physical space and acts as supervisory control to make machines self-configure and self-adaptive. This stage acts as resilience control system (RCS) to apply the corrective and preventive decisions, which has been made in cognition level, to the monitored system.

3.4 Augmented reality (AR)

Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are "augmented" by computer-generated or extracted real-world sensory input such as sound, video, graphics, haptics or GPS data [36].

R. Masoni [37] states that according to late big advancements in AR enabling technologies, e.g. cameras, sensors, tracking algorithms and visualization technologies, and thanks to the evolution of information and communication technologies in general, AR now has entered the consumer market. At the same time, AR has been recognized one of the leading technology in the 4th industrial revolution.

In terms of maintenance AR can contribute to the manually done maintenance, so step 1 to step 4 as stated in section 2.3 Maintenance towards intelligence and automation.

The company Atheer is a world leader in AR software and technology and states the following benefits of using AR technology for manual maintenance [38].

Smart glasses help mechanics fix equipment in three key ways:

 If the mechanic needs manuals, documentation, or other information, these tools are readily available and accessible on virtual screens right in his field of vision. This helps the mechanic do his job better and faster, and the information provided can be fully contextual. Information can be accessed and manipulated hands-free, even while wearing gloves. Gestures made in

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the air in the visual field are what enable the wearer of the smart glasses to navigate through virtual screens of information.

 The mechanic can use collaboration software to collaborate with an expert in another location. Through his smart glasses, the mechanic can share what he sees with a remote expert. He can then interact with the expert, viewing drawings on one of his virtual screens or receiving specific guidance overlaid onto the area in need of repair. This is the significant benefit that Augmented Reality delivers: taking something in the real-world visual field and using

computer-aided technology to overlay information onto it. An example is shown in Figure 11.  Step by step instructions can be established, followed, and navigated via voice or hand

gestures. Directions can be made richer, contextual, and conditional based upon the completion of prior steps. See Figure 12 for an example.

Figure 11 Collaborate with expert in another location [38]

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Existing technologies, like looking things up on a laptop, aren’t practical when a mechanic has his or her hands full of tools, is wearing gloves, or has to be in an awkward position. With smart glasses, the mechanic works smarter, faster, and collaboratively if needed, reducing expensive delays. The cost-savings as well as quality-control and error reductions are significant. For example, addressing needed repairs often requires that experts travel to very remote places. This typically means significant travel and downtime costs. Instead of having scarce experts traveling all over the world to fix things, industrial enterprises will be able to let those experts work from a central location and travel far less often. Using smart glasses and AR to provide remote support, those experts can “virtually” leverage their expertise to help customers directly – or help less-skilled local personnel to help those customers – in handling critical, time-sensitive work. There are possibilities for completely re-engineering

industrial enterprise processes, thanks to the capabilities of smart glasses. The company states that anyone who sells complex machines that are distributed – such as semiconductors, electronics manufacturing, construction equipment, or large-scale printers – can reduce costs and improve customer satisfaction using the technology available today through such smart glasses.

According to I-scoop [39] AR can also contribute for  Retail showcasing

 Safety (creating digital environments)  Training purposes

These factors are considered not to be in the scope of this report and therefore not further discussed, although the use of AR for safety and training purposes regarding maintenance could be an

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4. The container terminal

To have a brief understanding of how the maintenance is executed on terminal equipment and how intelligent maintenance towards industry 4.0 could contribute, a brief understanding of how terminals operate and why is necessary. Firstly, the container terminal in general and current trends will be discussed in section 4.1, then the container terminal equipment will be discussed in section 4.2.

4.1 The container terminal in general

According to D. Steenken. [40] in general terms, container terminals can be described as open systems of material flow with two external interfaces. These interfaces are the quayside with loading and unloading of ships, and the landside where containers are loaded and unloaded on/off trucks and trains. Within a terminal, containers are stored in stacks.

The container terminal is a rather complicated system with several interrelated types of operations, numerous controllable objects (equipment) and thousands of plannable items (jobs, containers). Thus, the terminal is often subdivided into several subsystems according to the related operations and the equipment involved. The whole terminal system is viewed to consist of the ship-to-shore subsystem, the waterside horizontal-transport subsystem, the storage subsystem and the hinterland-connection subsystem.

Despite this division into several subsystems, the handling capacity and the performance of the whole terminal system is determined by all the subsystems, which means that the subsystem with the smallest handling capacity determines—as the bottleneck—the handling capacity of the container terminal. Since the different subsystems are linked with one another, each subsystem should be designed and managed in such a way that the connected subsystem(s) may be operated most efficiently.

4.1.1 The container terminal layout

According to N. Kemme [41] there are hundreds of container terminals with different layouts, different container-handling concepts and different types of equipment around the world. Nevertheless, most terminals have a comparable arrangement of their subsystems and facilities, which is schematically shown in Figure 13.

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Figure 13 Schematic terminal layout [41].

Of course, the ship-to-shore subsystem is located at the waterside edge of the terminal where quay cranes are used to load and discharge vessels and barges. In general, the ship-to-shore subsystem is followed by the horizontal-transport subsystem, which is responsible for the transport of full and empty containers between the quay cranes and the storage subsystem. Usually, this horizontal transport is executed by different types of transport vehicles. These will be discussed in section 4.3. The storage subsystem is the place on the terminal where containers are temporarily stored. Besides the regular storage area, most container terminals exhibit a special empty depot where empty containers are stored according to the needs of the shipping lines. In addition, most facilities for the added services that are offered by container terminals may be assigned to the storage subsystem. Here, a container freight services (CFS) and facilities for maintenance and repair of containers are linked with the storage subsystem. Due to its decoupling function between waterside and landside terminal operations, the storage subsystem is located in the center of the terminal. According to its main function, the regular storage area takes up most of the space of the storage subsystem. On the landside, the storage subsystem is followed by the hinterland-connection subsystem, which fulfils the function of an interface between the terminal and its hinterland. As both external trucks (XTs) and trains act as landside connecting modes of transportation of seaport container terminals, the hinterland-connection subsystem may comprise facilities for both modes. Trains are loaded and discharged at the rail station of the terminal by special equipment. XTs enter the terminal at the gate facilities, where they are checked and administrative tasks are fulfilled. Next, the XTs drive on dedicated streets or driving areas to a handover area where the relevant container is loaded onto or discharged from the XT by special terminal equipment.

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The two external interfaces allow the container handling in a decoupled way regarding quayside and landside operations. The imported containers don’t have to leave the terminal via the landside but can also be exported via another vessel, what is called transshipment [42].

4.1.2 Terminal performance

According to J. Rijsenbrij [43] the overall performance of a terminal is determined by a lot of factors and depends on actors with their own interests. An overview of these actors and their interests is shown in Figure 14.

Figure 14 Performance of a terminal [43].

4.1.3 Container terminal trends and the automated container terminal

The IMS center did a survey [9] in current trends in container terminals which are stated in ballpoints below:

 Container terminals in today’s climate are facing dauting times with liner operators demanding change and greater flexibility in services. Terminals have to adapt to these changes and mould new techniques to meet the changing market conditions.

 More and more container lines are deploying the ultra large container vessels (13.000-18.000 TEU(+)) which bring significant pressures on the container terminals

 Growing pressure to maintain vessel turn-times and reduce delays for calls are driving factors towards greater efficiencies.

 Terminals are facing environmental restrictions which will have an impact on both operational equipment and expansions

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 Container terminals restrictions on TEU capacities increases greater demand for tighter control and better management of space as well as effective handling and processing of every unit that passes through the facility.

 Terminals have to maxima’s resources and assets and are continually planning for maximum throughput and efficient cargo processing.

 Terminals are under pressure to perform and provide a stable and sustainable rate of return for their investors.

 Global carriers are looking for lower rates and better service levels from their terminal

operators. This ever increasing pressure is driving terminals to adopt newer methods for their container operations.

 This is why new handling technology, software and effective planning are the answers for the terminals management to consider.

These trends have led to more automated or almost fully automated terminals as also the company ABB endorses. The company ABB states that as container terminals aim for more efficient operation and higher productivity, automation is making major strides all over the world. More and more container terminals are adopting automated solutions to meet the challenge of larger ships, taller cranes and bigger call sizes [44].

According to A. Mari Martin [45] the launch of the ECT Delta Terminal in the Port of Rotterdam in the Netherlands in 1993 introduced the concept of “automated terminals” to refer to the highest level of automation to date. It was equipped with Automated Stacking Cranes (ASCs) and Automated Guided Vehicles (AGVs), allowing it to manage, without operators, the handling of storage and interchange equipment respectively. Since the nineties many port container terminals (PCTs) have embraced automation, consolidating itself as a global and permanent trend in the sector

According to A. Maria Martin [45] an automated container terminal can be defined as a terminal that runs as an automated process some or all of discharging of ships, transport, and yard equipment operations. “automated terminals” refers to terminals that have automated their storage equipment and the interchange between subsystems, this is only one of the many automation trends in PCTs and the general trend is headed for higher levels of automation that go beyond the borders of terminal yards to involve all operations. In general terms this wider development includes:

 The automation of gates;  The automation of yards;  The automation of quay cranes.

In fact, the first automations implemented in PCTs and the most advanced automation systems in today’s market are those related to the processes that take place at the terminal gates. In this sense efforts are still being made to improve data gathering systems in the terminal-logistics chain interface. This interest to automate data gathering is common for inland and maritime gates, although it is the

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former of the two that captures more volume of data due to the atomization of transport means. Yard automation is the most apparent and obvious trend in PCTs. The automated technology of storage and transfer equipment is similar and handles the automation of the inventory of the stock of containers located in the yard and the monitoring of equipment in real time. It is evolving towards the design of handling systems that are increasingly more self-sufficient in operational and economical terms such as those composed by the combination of ASCs + AGVs, ASCs + ALVs (Automated Lifting Vehicles) or ASCs + AShC (Automated Shuttle Carriers), amongst others. The list of automated and semi-automated terminals shown in Figure 15 has not stopped growing over the past years and it will continue doing so given increased investments being made in automation projects and the

construction of new automated terminals in different geographic areas. These port facilities dispose of state-of-the-art currently available yard automation technologies, even though not all of them have opted for the same technological solution in terms of design.

Figure 15 Automated and semi-automated PCTs [45].

Finally, quay cranes are the elements of operations whose automation is less developed, although it is foreseen that they will be the equipment with the biggest technological advance during the coming years. To date efforts to automate quay cranes have resulted in minor automations which,

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implemented in factories at origin or by means of retrofitting, can mechanize some of the functions that until then depended on the ability of crane operators. These are focused on the control of the movements of spreaders, both involuntary (sway and skew) as well as their pathway, and the

connection between quay cranes and transfer equipment. In parallel, terminals and manufacturers are testing systems that would manifest a qualitative technological leap for the automation of STS cranes. In 2014 the AMPT Maasvlakte 2 terminal come into operation in the Port of Rotterdam (The

Netherlands). This terminal will boast the highest level of automation reached to date, combining the automation of gates and yards with the nearly complete automation of the pathway made by the trolley and spreader of quay cranes. This will be assisted by remote control from the operations control tower at the terminal only in the last meters to the ship. However, even though the current trends in the automation of PCTs are heading towards a total effective automation of these facilities, there exist numerous possible options between the total automation and the conventional manual management of PCTs, including assisting equipment remotely.

These ports use automated equipment discussed in section 4.2 and need an advanced terminals operation systems(TOS) and information technology (IT) to control assign the automatic procedures. According to the company RBS [46] a Terminal Operating System (TOS) is a key part of a terminal and primarily aims to control the movement and storage of various types of cargo in and around a container terminal, port or inland depot. Terminal Operating Systems often utilize other technologies such as internet, EDI processing, mobile computers / mobile devices, wireless LANs, Radio-frequency identification (RFID) and DGPS to efficiently monitor the flow of products in, out and around the terminal. Data is either batch synchronization with, or a real-time wireless transmission to a central database. The database can then provide useful reports about the status of goods, its locations, as well as the CHE container handling equipment in the terminal. A TOS also enables a terminal to make better use of its assets, labor and equipment, plan workload, and get up to the minute information which allows for more timely and cost-effective decision making. The objective of a TOS is to provide a set of computerized procedures to manage cargo, machines and people within the facility, in order to enable a seamless, efficient and effective management of the facility. A TOS should support a streamlined operation, from high level vessel/berth planning down to equipment and work instruction execution.

Apart from the above, a TOS should provide a terminal with the full suite of operational capabilities, including Yard management; Vessel management; Berth management; Crane allocation; Container Handling Equipment (CHE) management; Rail management; Gate management; Booking and Pre-Advice of containers; Truck management and enquiry; User security and access control; and Reports A TOS should also have optional interfaces to external third party systems (e.g.: Financial systems, radio data terminals, REFCON Reefer Monitoring system, Electronic weighing scale, DGPS systems etc.). There are currently not many TOS systems available within the market offering true real-time

Intelligent maintenance for container terminal equipment towards Industry 4.0

Summary

Table of Contents

1. Introduction

2. Maintenance

2.1 Categorizing Maintenance

2.1.1 Preventive maintenance

2.1.2 Random maintenance

2.1.3 Corrective maintenance

2.1.4 Predictive maintenance (PdM)

2.2 Management systems

2.3 Maintenance towards intelligence and automation

2.4 Intelligent maintenance systems and e-Maintenance

2.4.1 Intelligent Maintenance

2.4.2 E-maintenance

3. Industry 4.0

3.1 Internet of Things (IoT)

3.2 Big data

3.3 Cyber Physical Systems (CPS)

3.4 Augmented reality (AR)

4. The container terminal

4.1 The container terminal in general

4.1.1 The container terminal layout

4.1.2 Terminal performance

4.1.3 Container terminal trends and the automated container terminal