Advances in Transdisciplinary Engineering

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Delft University of Technology

Advances in Transdisciplinary Engineering

Curran, Ricky

Publication date 2017

Document Version Final published version Citation (APA)

Curran, R. (Ed.) (2017). Advances in Transdisciplinary Engineering. (Advances in Transdisciplinary Engineering; Vol. 5). IOS Press.

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TRANSDISCIPLINARY ENGINEERING: A PARADIGM

SHIFT

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Advances in Transdisciplinary Engineering

Advances in Transdisciplinary Engineering (ATDE) is a peer-reviewed book series covering the developments in the key application areas in product quality, production efficiency and overall customer satisfaction.

ATDE will focus on theoretical, experimental and case history-based research, and its application in engineering practice. The series will include proceedings and edited volumes of interest to researchers in academia, as well as professional engineers working in industry.

Editor-in-Chief

Josip Stjepandić, PROSTEP AG, Darmstadt, Germany Co-Editor-in-Chief

Richard Curran, TU Delft, The Netherlands Advisory Board

Cees Bil, RMIT University, Australia

Milton Borsato, Federal University of Technology – Parana, Brazil Shuo-Yan Chou, Taiwan Tech, Taiwan, China

Fredrik Elgh, Jönköping University Parisa Ghodous, University of Lyon, France

Kazuo Hiekata, University of Tokyo, Japan John Mo, RMIT University, Australia Essam Shehab, Cranfield University, UK

Mike Sobolewski, TTU, Texas, USA Amy Trappey, NTUT, Taiwan, China Wim J.C. Verhagen, TU Delft, The Netherlands Wensheng Xu, Beijing Jiaotong University, China

Volume 5

Recently published in this series

Vol. 4. M. Borsato, N. Wognum, M. Peruzzini, J. Stjepandić and W.J.C. Verhagen (Eds.), Transdisciplinary Engineering: Crossing Boundaries – Proceedings of the 23rd ISPE Inc. International Conference on Transdisciplinary Engineering, October 3–7, 2016 Vol. 3. Y.M. Goh and K. Case (Eds.), Advances in Manufacturing Technology XXX –

Proceedings of the 14th International Conference on Manufacturing Research, Incorporating the 31st National Conference on Manufacturing Research, September 6–8, 2016, Loughborough University, UK

Vol. 2. R. Curran, N. Wognum, M. Borsato, J. Stjepandić and W.J.C. Verhagen (Eds.), Transdisciplinary Lifecycle Analysis of Systems – Proceedings of the 22nd ISPE Inc. International Conference on Concurrent Engineering, July 20–23, 2015

ISSN 2352-751X (print) ISSN 2352-7528 (online)

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Transdisciplinary Engineering:

A Paradigm Shift

Proceedings of the 24th ISPE Inc. International Conference on

Transdisciplinary Engineering, July 10–14, 2017

Edited by

Chun-Hsien Chen

Nanyang Technological University, Singapore

Amy C. Trappey

National Tsing Hua University, Taiwan

Margherita Peruzzini

University of Modena and Reggio Emilia, Italy

Josip Stjepandić

PROSTEP AG, Germany

and

Nel Wognum

TU Delft, The Netherlands

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This book is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

ISBN 978-1-61499-778-8 (print) ISBN 978-1-61499-779-5 (online)

Library of Congress Control Number: 2017945959

Publisher IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: order@iospress.nl

For book sales in the USA and Canada: IOS Press, Inc.

6751 Tepper Drive Clifton, VA 20124 USA Tel.: +1 703 830 6300 Fax: +1 703 830 2300 sales@iospress.com LEGAL NOTICE

The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS

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Preface

This book of proceedings contains papers peer reviewed and accepted for the 24th ISPE Inc. International Conference on Transdisciplinary (formerly: Concurrent) Engi-neering, held at the Nanyang Technological University, Singapore, July 10–14, 2017. This is the sixth issue of the newly introduced series “Advances in Transdisciplinary Engineering”, which publishes the proceedings of the TE (formerly: CE) conference series and accompanied events. The TE/CE conference series is organized annually by the International Society of Productivity Enhancement (ISPE, Inc.) and constitutes an important forum for international scientific exchange on transdisciplinary concurrent engineering and collaborative enterprises. These international conferences attract a significant number of researchers, industry experts and students, as well as government representatives, who are interested in the recent advances in transdisciplinary concur-rent engineering research, advancements and applications.

Developed in the 80’s, the CE approach is based on the concept that different phases of a product life cycle should be conducted concurrently and initiated as early as possible within the Product Creation Process (PCP), including the implications of this approach within the extended enterprise and networks. The main goal of CE is to in-crease the efficiency and effectiveness of the PCP and to reduce errors in the later phases, as well as to incorporate considerations for the full lifecycle, through-life oper-ations, and environmental issues. In the past decades, CE has become the substantive basic methodology in many industries (e.g., automotive, aerospace, machinery, ship-building, consumer goods, process industry, environmental engineering) and is also adopted in the development of new services and service support.

The initial basic CE concepts have matured and have become the foundations of many new ideas, methodologies, initiatives, approaches and tools. Generally, the cur-rent CE focus concentrates on enterprise collaboration and its many diffecur-rent elements; from integrating people and processes to very specific complete multi/inter/trans-disciplinary solutions. Current research on CE is driven again by many factors like in-creased customer demands, globalization, (international) collaboration and environ-mental strategies. The successful application of CE in the past opens also the perspec-tive for future applications like overcoming natural catastrophes, sustainable mobility concepts with electrical vehicles, and intensive, integrated, data processing. Due to the increasing importance of transdisciplinarity, the board of ISPE, Inc. has decided to re-name the conference series in “Transdisciplinary Engineering”.

The TE2017 Organizing Committee has identified 31 thematic areas within CE and launched a Call For Papers accordingly, with resulting submissions submitted from all continents of the world. The conference is entitled: “Transdisciplinary engineering: a paradigm shift”. This title reflects the variety of processes and methods which influ-ences the modern product creation. Finally, the submissions as well as invited talks were collated into 16 streams led by outstanding researchers and practitioners.

The Proceedings contains 120 peer-reviewed papers by authors from 27 countries. These papers range from the theoretical, conceptual to strongly pragmatic addressing industrial best practice. The involvement of more than 15 companies from many indus-tries in the presented papers gives additional importance to this conference.

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This book on ‘Transdisciplinary engineering: a paradigm shift’ is directed at three constituencies: researchers, design practitioners, and educators. Researchers will bene-fit from the latest research results and knowledge of product creation processes and related methodologies. Engineering professionals and practitioners will learn from the current state of the art in concurrent engineering practice, new approaches, methods, tools and their applications. The educators in the CE community gather the latest ad-vances and methodologies for dissemination in engineering curricula, while the com-munity also encourages young educators to bring new ideas into the field.

The proceedings are subdivided into sixteen parts, reflecting the themes addressed in the conference programme:

Part 1 contains papers in the theme Air Transport and Traffic Operations and Man-agement addressing operational manMan-agement and traffic control issues.

Part 2 contains contributions on Risk-aware Supply Chain Intelligence addressing operational and management issues in client-supplier relationships.

Part 3 illustrates some approaches to Product Innovation and Marketing Manage-ment. Papers included in this part address issues, like brand loyalty, consumer readiness, and consumer involvement in innovation processes.

Part 4, Human Factors in Design, an area with growing interest, contains papers on research into, for example, visualization, human behavior with products, and ergo-nomics.

Part 5, Human Engineering, contains papers, amongst others, on the design of in-telligent devices like prostheses.

Part 6 addresses the theme Design Methods and Tools with papers on data and methods for specific design processes.

Part 7 contains papers on the theme Decision Supporting Tools and Methods. In this part subjects like methods and tools for mass customization are addressed in-cluding decision-making approaches.

Part 8 deals with the Concurrent Engineering. This part contains various approach-es, methods, tools for planning, managing and executing a transdisciplinary engi-neering process.

Part 9, Knowledge-based Engineering, addresses a variety of approaches to capture, process, manage, use and disseminate knowledge in a transdisciplinary engineering process.

Part 10 is entitled Collaborative Engineering and contains papers on research into methods and tools for the initial phases of the design process in different applica-tion areas.

Part 11 contains papers on Engineering for Sustainability: cost-optimal, resource-efficient and eco-design and engineering.

Part 12 contains contributions in the area of Service Design, addressing the notion of systems, as well as the design of service systems and logistics.

Part 13 focuses on Digital Manufacturing with an emphasis on production process-es, scheduling, maintenance, and work planning.

Part 14 addresses the topic of Design Automation, addressing topics like modeling automation, process automation, interoperability and data-driven design.

Part 15 contains papers on the theme Artificial Intelligence and Data Analytics with an emphasis on modeling.

Part 16 outlines the importance of Smart Systems and the Internet of Things. Spe-cial attention will be given to Cyber Physical Systems, Industry 4.0 and cloud ob-jects.

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We acknowledge the high quality contributions of all authors to this book and the work of the members of the International Program Committee who assisted with the blind peer-review of the original papers submitted and presented at the conference. Readers are sincerely invited to consider all of the contributions made by this year’s participants through the presentation of TE2017 papers collated into this book of pro-ceedings. We hope that they will be further inspired in their work for disseminating their ideas for new approaches for sustainable, integrated, product development in a multi-disciplinary environment within the ISPE, Inc. community.

Chun-Hsien Chen, General Chair Nanyang Technological University, Singapore Amy C. Trappey, Co-General Chair National Tsing Hua University, Taiwan Margherita Peruzzini, Program Chair University of Modena and Reggio Emilia, Italy Josip Stjepandić, Co-Program Chair PROSTEP AG, Germany Nel Wognum, Co-Program Chair TU Delft, The Netherlands

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Committees

Organizing Committee

Chun-Hsien Chen (General Chair)

Nanyang Technological University, Singapore Amy Trappey (General Co-Chair)

National Tsing Hua University, Taiwan Wolfgang Muller-Wittig (General Co-Chair) Fraunhofer, Singapore

Margherita Peruzzini (Program Chair)

University of Modena and Reggio Emilia, Italy Marcello Pellicciari (Program Co-Chair) University of Modena and Reggio Emilia, Italy Josip Stjepandić (Program Co-Chair)

PROSTEP AG, Germany

Seung Ki Moon (Program Co-Chair)

Nanyang Technological University, Singapore Nel Wognum (Program Co-Chair)

Wageningen University, The Netherlands Cees Bil (Program Co-Chair)

Royal Melbourne Institute of Technology, Australia Cindy Wang I-Hsuan (Program Co-Chair)

Nanyang Technological University, Singapore

ISPE Steering Committee

Ricky Curran, TU Delft, The Netherlands Michael Sobolewski, TTU, Texas, USA Essam Shehab, Cranfield University, UK

Amy Trappey, National Tsing Hua University (NTUT), Taiwan Cees Bil, RMIT University, Australia

Chun-Hsien Chen, Nanyang Technological University, Singapore Fredrik Elgh, Jönköping University, Sweden

Milton Borsato, Federal University of Technology, Paraná-Curitiba, Brazil Josip Stjepandić, PROSTEP AG, Germany

John Mo, RMIT University, Australia Nel Wognum, The Netherlands

Shuichi Fukuda, Stanford University, USA

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Shuo-Yan Chou, Peking University, China Parisa Ghodous, University of Lyon, France Kazuo Hiekata, the University of Tokyo, Japan Ricardo Gonçalves, UNINOVA, Portugal Ahmed Al-Ashaab, Cranfield University, UK

Jerzy Pokojski, Warsaw University of Technology (SIMR), Poland Rajkumar Roy, Cranfield University, UK

Geilson Loureiro, INPE, Brazil

Ahmed Al-Ashaab, Cranfield University, UK

Gang Shen, Huazhong University of Science and Technology, Wuhan, China

International Program Committee

Ada Chang

Institute for Information Industry, Taiwan

Adina Cretan

Nicolae Titulescu University of Bucharest, Romania

Alain-Jerome Fougeres

Université de Technologie de Belfort-Montbéliard, France

Bernard Chen

Monash University, Australia Bong-Shik Yun

Nambu University, South Korea Boyd Nicholds

RMIT University, Australia Bryan R. Moser

Massachussets Institute of Technology, USA

Carla Estorilio

Federal University of Technology, Paraná, Brazil

Catarina Ferreira Da Silva Universite Claude Bernard Lyion, France

Charles Trappey

National Chiao Tung University, Taiwan

Chengqi Xue

Southeast University, China

Chien-Chih Wang

Ming Chi University of Technology, Taiwan

Chihhsuan Wang

National Chiao Tung University, Taiwan

Chin Yuan Fan

National Applied Research Laboratories, Taiwan Christoffer Levandowski

Chalmers University of Technology, Sweden

Chun-Hsien Chen

Cindy Wu

Open University of Kaohsiung, Taiwan

Dag Raudberget

Chalmers University of Technology, Sweden

Danni Chang

Shanghai Jiao Tong University, China

Denis Tsygankov

Ulyanovsk State Technical University, Russia

Egon Ostrosi

Université de Technologie de Belfort-Montbéliard, France

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Essam Shehab

Cranfield University, Unitied Kingdom Eva Shih

National Taipei College of Business, Taiwan

Fang Jia

Shenzhen University, China Fei Hu

Guangdong University of Technology, China

Fernando Deschamps

Pontifical Catholic University of Paraná, Brazil

Fredrik Elgh

Jönköping University, Sweden Gang Shen

Huazhong University of Science and Technology, China

George Q. Huang

The University of Hong Kong, China

German Urrego

University of Antioquia, Colombia Germano Kienbaum

INPE, Brazil

Giuliani Paulineli Garbi

Brazilian Institute of Space Research, Brazil

Gloria Lucia Giraldo Gómez Universidad Nacional de Colombia, Colombia

Goran Šagi

University of Zagreb, Croatia Hsiao Shih-Wen

National Cheng Kung University, Taiwan

Jerzy Pokojski

Warsaw University of Technology, Poland

Jianxin Cheng

East China University of Science and Technology, China

Joao Adalbero Pereira

COPEL Companhia Paranaense de Energia, Brazil

Joel Johansson

Jönköping University, Sweden John Mo

RMIT University, Australia John Bang Mathiasen Aarhus University, Denmark Jose Rios

Madrid Polytechnic University, Spain

Jože Duhovnik

University of Ljubljana, Slovenia Jože Tavčar

University of Ljubljana, Slovenia Junliang He

Shanghai Maritime University, China Junnan Ye

Junnan Yu

Shanghai Jiao Tong University, China Kazuo Hiekata

The University of Tokyo, Japan Kenji Tanaka

The University of Tokyo, Japan Le Xi

Leonid Kamalow

Ulyanovsk State Technical University, Russia

Luiz Fernando Campos Universidade Positivo, Brazil

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Marcello Pellicciari

University of Modena and Reggio Emilia, Italy

Marek Jemala

Slovak University of Technology, Slovakia

Maria Lucia Miyake Okumura Pontifical Catholic University of Parana, Brazil

Marija Vidić

University of Mostar, Bosnia and Herzegovina

Mike Sobolewski

US Air Force Research Lab Milton Borsato

Federal University of Technology, Brazil

Ming-Chuan Chiu

National Tsing Hua University, Taiwan

Moisés Dutra

Federal University of Santa Catarina, Brazil

Nicolas Figay Airbus SAS Nozomu Mishima Akita University, Japan Osiris Canciglieri

Pontifical Catholic University of Paraná, Brazil

Parisa Ghodous

Universite Claude Bernard Lyion, France

Pekka Siltanen

VTT Technical Research Centre of Finland, Finland

Pisut Koomsap

Asian Institute of Technology, Thailand

Rajkumar Roy

Cranfield University, United Kingdom Ray Y. Zhong

University of Auckland, New Zealand Ricardo Gonçalves

Uninova Richard Curran

TU Delft, The Netherlands Roland Stolt

Jönköping University, Sweden Ronald Beckett

Deakin University, Austalia Shuai Yang

Guangdong University of Technology, China

Shuichi Fukuda Keio University, Japan Shuo-Yan Chou

National Taiwan University of Science and Technology, Taiwan

Teruaki Ito

Tokushima University, Japan Timo Wekerle

Instituto Tecnologico de Aeronautica, Brazil

Ting Han

Shanghai Jiao Tong University, China Vitaly Semenov

Institute for System Programming RAS

Vitor de Souza

Federal University of Technology – Parana (UTFPR), Brazil

Wensheng Xu

Beijing Jiaotong University, China Wojciech Skarka

Silesian University of Technology, Poland

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Xia Wei

Shenzhen University, China Xiaojia Zhao

TU Delft, The Netherlands Xingyu Chen

Shenzhen University, China Xun Xu

University of Auckland, New Zealand Yao Qin

Macao University of Science and Technology, Macao

Yu Wang

Tongji University, China Yunfeng Huo

HUO DESIGN Ze En Chien

National Cheng Kung University, Taiwan

Zhangfan Shen

Southeast University, China

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Organizers

International Society for Productivity Enhancement, Inc. Nanyang Technological University, Singapore

Past Concurrent Engineering conferences

2016: Curitiba, Brazil 2015: Delft, The Netherlands 2014: Beijing, China 2013: Melbourne, Australia 2012: Trier, Germany 2011: Boston, USA 2010: Cracow, Poland 2009: Taipei, Taiwan 2008: Belfast, UK

2007: São José dos Campos, Brazil 2006: Antibes-Juan les Pins, France 2005: Dallas, USA 2004: Beijing, China 2003: Madeira, Portugal 2002: Cranfield, UK 2001: Anaheim, USA 2000: Lyon, France 1999: Bath, UK 1998: Tokyo, Japan 1997: Rochester, USA 1996: Toronto, Canada 1995: McLean, USA 1994: Pittsburgh, USA xiv

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Part 1 Air Transport and Traffic Operations and

Management

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Component-Based Data-Driven Predictive

Maintenance to Reduce Unscheduled

Maintenance Events

Wim J.C. VERHAGEN1, Lennaert W.M. DE BOER and Richard CURRAN

Faculty of Aerospace Engineering, Technical University of Delft, The Netherlands

Abstract. Costs associated with unscheduled and preventive maintenance can

contribute significantly to an airline’s expenditure. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based approaches that incorporate failure times as the primary stochastic variable, with additional strict assumptions regarding independence of events and underlying distributions of failure phenomena. This foregoes the complex nature of aircraft operations, where a whole range of operational factors may influence the probability of occurrence of a maintenance event. The aim of this research is to identify operational factors affecting component reliability and to assess whether these can be used to reduce the number of unscheduled occurrences (i.e. failures). To do so, a data-driven approach is adopted where historical operational and maintenance data is gathered and analysed to identify operational factors with a measurable influence on maintenance event occurrence. Both time-independent and time-dependent Proportional Hazard Models (PHMs), models which incorporate operational factors as covariates, are employed to generate reliability estimates. Results obtained from analysing historical data of a set of ten components with respect to unscheduled removals indicates that adopting new maintenance schedules, derived from the proposed reliability models, could reduce the number of unscheduled occurrences by approximately 37%. The potential benefits of adopting the proposed strategy are extensive. Nonetheless, numerous assumptions have been introduced to overcome challenges imposed by the complex nature of the data. To overcome these challenges, recommendations are made for future development of the proposed approach.

Keywords. Predictive maintenance, unscheduled maintenance, Proportional

Hazard Model

Introduction

Costs associated with maintenance can contribute significantly to an airline’s expenditure; historical estimates for maintenance cost range between 10 – 15% of the overall expenditure incurred by airlines [1]. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based approaches that incorporate failure times as the primary stochastic variable [2]. Such approaches assume simple binary behaviour in terms of reliability: a component works or it does not. In addition, strict assumptions regarding

1_{Corresponding Author, Mail}_{: w.j.c.verhagen@tudelft.nl}

Transdisciplinary Engineering: A Paradigm Shift C. Chen et al. (Eds.)

This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/978-1-61499-779-5-3

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(in)dependence of events and underlying distributions of associated failure phenomena are frequently made [3], which may be unwarranted in some cases. In relation to the aerospace domain, a major limiting factor of existing statistically-based approaches is that these forgo the complex nature of aircraft operations, where a whole range of operational factors may influence the probability of occurrence of a maintenance event. For instance, aircraft operating from hot, sandy airports or regions have very different conditions of use than aircraft operating from cold, wet airports, which leads to different failure modes and times for components.

The aim of this research is to improve statistical reliability assessment in aircraft maintenance by incorporating the effect of operational factors. To do so, operational factors affecting component reliability are identified and assessed for their capability to reduce the number of unscheduled occurrences (i.e., failures). A data-driven approach is adopted where historical operational and maintenance data is gathered and analysed to identify operational factors with a measurable influence on maintenance event occurrence. The identification of these explanatory variables constitutes the primary contribution to the state of the art. Additionally, both independent and time-dependent Proportional Hazard Models (PHMs) are employed to generate reliability estimates, as these statistical models do have the possibility to incorporate explanatory variables as covariates.

The structure of this paper reflects this focus. In Section 1, a brief theoretical context is given. In Section 2, the modelling approach is given, including a discussion of the method to identify relevant operational factors and formulation of the reliability models used in this research. The next Section provides results for a set of selected components. Finally, some conclusions and indications for future research are presented.

1. Theoretical context

Time-based reliability models use component age (time) to model reliability. In its simplest form its lifetime distribution function F(t) and probability density function f(t) are based on common statistical distribution functions (e.g. exponential, normal, log-normal). For more complex components where sequences of random variables are involved, such as repairables, statistical models can be reformulated to include a renewal parameter [3]. Research has shown that type II General Renewal Processes (GRP-II) generally provide better estimates than [Non] Homogeneous Poison Processes

([N]HPPs) and Renewal Processes (RPs) [4]. In GRP models the ith failure is

formulated using the previous failure time ((i-1) th failure) and with a renewal function,

derived from the renewal parameter. Common weaknesses of time-based reliability models include a lack of capability to incorporate explanatory variables and a lack of representation of multiple degradation states.

Proportional Hazard Models, also known as Cox models, extend time-based models by introducing covariates [5]. The standard (time-based) statistical hazard function is reformulated to introduce covariates and corresponding parameters, as given in equation 1.

0

( , | , ) ( | ) TZ

t Z t eE

O T E O T (1)

W.J.C. Verhagen et al. / Component-Based Data-Driven Predictive Maintenance

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Z are the covariates corresponding to failure t, λ0(t) is the underlying hazard

function (e.g. normal distribution), θ (θ0, θ1, …, θi) denotes the unknown parameters of

the underlying distribution function, and β (β0, β1…, βj) denotes the unknown

parameters corresponding to each covariate. This equation can readily be reformulated to incorporate time dependent covariates Z(t), at the cost of computational complexity.

PHM models have been employed succesfully in research before, for instance in aerospace domain applications [6,7], but to a limited degree in practice. However, current developments in aircraft operations and maintenance – in particular with respect to increased storage and availability of sensor data to characterise operational conditions during flight – open up the possibility to revisit these models for a structured, automated application towards reliability estimation incorporating operational variables.

2. Modelling approach

The approach used to model and analyse reliability of components, including the effect of operational factors, is highlighted in Section 2.1. Subsequently, two steps in the approach are detailed further: identification of relevant operational factors (Section 2.2) and reliability modeling (Section 2.3).

2.1. General modelling and analysis approach

The general modelling and analysis approach adopted in this study is shown in Figure 1. As visualized, the modelling approach consists of five main blocks:

1) Program initiation: this step addresses importing fleet-wide maintenance and flight datasets, the identification of component-specific data in the wider maintenance dataset, the extraction of component-related flight data from the flight dataset, and, as a last and critical step, extraction and characterisation of component-related maintenance events. These events can be of type Failure, in which a component has failed unexpectedly and has required unscheduled corrective maintenance, and Censored, in which a component has been replaced according to schedule at a specified time interval.

2) Flight identification: flight identification helps to address the following hypothesis: the heavier the operational use of components, the higher the

probability of component failure. Flight identification identifies flights which may have had an influence on unexpected component failure. A heuristic has

been developed to identify a set of flights which can be associated with a particular failure event on a particular day, instead of an individual flight. 3) Data analysis: in step 3, two distinct approaches are used to identify

operational factors of influence towards component failure: extreme value analysis and maximum difference analysis. These approaches are discussed in more detail in Section 2.2.

4) Reliability modelling: In step 4, a set of reliability models is applied to analyse the component dataset(s). A standard statistical approach (incorporating failure time as the single variable of interest) is employed to give baseline predictions, in accordance with current industry standards. In addition, two variants of the Proportional Hazard Model (PHM) are employed to account for the influence of operational factors, as identified in step 3. These variants are discussed in more detail in Section 3.3.

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5) Future predictions: The final step in the approach concerns the generation of expected failure times using the reliability models established in the previous step. By predicting flight utilization and conditions, it is possible to estimate expected values for failure times for specific components, which can be used to adjust maintenance scheduling.

Figure 1. Modelling and analysis approach.

2.2. Identification of relevant operational factors

As mentioned in step 3 of the overall approach, it is critical to know which operational factors can have a measurable influence on component reliability behaviour over time. Given historical data regarding this behaviour, and operational data which can be linked to the component utilization over time, it becomes possible to identify which operational factors influence reliability. In line with the general approach, it is assumed that the identification of related flights towards a component maintenance event has been succesful, leading to a small subset of flights with potential relation to the event.

2.2.1. Extreme Value Analysis (EVA)

The focus of this module is to further narrow down the number of potentially related flights and assign one flight per failure event based on the occurrence of extreme values. In general terms, this module assesses (to a certain significance level) which operational factors were abnormally high. Extreme Value Analysis (EVA) optimises one flight variable at a time, searching for optimals in both the positive and negative direction. When optimising in the positive (negative) direction, flights with observation values x below (above) the mean μ were penalised by assigning a negative p value. This increases the probability that the selected flights experienced similar extremities in the operational variables. The optimisation problem is formulated as given in equations 2 and 3.

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, , Maximise , , { , } i D D D v ij v ij v i N j M z f p v V D u

¦ ¦

(2) Subject to , , 1, , { , } {0,1}, , , , { , } i D ij v j M D ij v i f i N D f i N j M v V D

¦

(3) With D_, ij v

f being a decision variable which represent optimal flight selection for variable

v in optimization direction D , where it should be noted that D_, ij v

f is 1 if flight j

corresponding to event i is the cause of failure, and is 0 if flight j corresponding to

event i is not the cause of failure. Furthermore, D_,

ij v

p expresses a probability that

variable v (representing an operational factor) in flight fijbelongs to group C, which

is the set of censored events (i.e., the events without failure). D is the optimization direction for variable v, where if D is negative (-), p values of variables v during flight

ij

f are penalized if observed value xij v, is above mean value

P

v, and if D is positive

(+), p values of variables v during flight f_ijare penalized if observed value x is below _ij

mean value

P

v.Furthermore, some sets are involved:N being a set of unscheduled

maintenance events (i.e, failures), Mibeing flights potentially related to failure event i,

and V being a set of operational factors.

Finally, note that in the equations above, p is a positive value in the interval [0 1]. To specify an optimization direction D, all p values are computed such that, depending on the direction, observations x_{ij v}_, below (or above)

P

vare penalized. Hence,

, , 2 , , , 2 (1 2 ( )) if (1 2 ( ) if ij v v ij v v v D ij v ij v v ij v v v x D P z x n p x D P z x n

P

V

P

V

u ! t u !

°

°°

®

°

°¯

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2.2.2. Maximum Difference Analysis (MDA)

The maximum difference module is important for time-independent PHM models, which focus on mean values during a component’s fail cycle (see Section 2.3). Its application is straightforward:

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1. Compute mean (per operational factor) of all flights related to failure events (Group F).

2. Extract mean and standard deviation (per operational factor) of all flights related to censored events (Group C).

3. Compute probability (per operational factor) of F belonging to C using Z-test (large population size and known standard error).

4. Extract operational factors that are least likely to belong to Group C.

Successful execution of EVA and MDA produces a selection of flights associated with failure events along with a reduced list of operational factors that are likely to be the root cause of failures. Examples are given in Section 3.

2.3. Reliability modeling approach

The failure events and associated operational factors constitute essential input for the reliability models, as described next. In total, three distinct models are employed to estimate reliability:

1) Generalized Renewal Process (GRP): a GRP-II model is formulated to serve as a baseline estimate using failure and censor times only. No operational factors are included into this model formulation. GRP-II models employ the concept of virtual age. Various underlying distributions have been tested and assessed for goodness-of-fit, including the normal, log-normal, logistic, gamma, exponential and Weibull distributions. This is justified as multiple components, with multiple failure modes, have been considered. For parameter estimation, maximum likelihood estimation (MLE) has been employed. The MLE routines have been adjusted to take into account censored data and multiple serial numbers per governing part number. To maximize the likelihood function, numerical algorithms have been employed as a closed-form solution to the likelihood function was not available. In particular, the Nelder-Mead and BFGS algorithms have been used [8].

2) Time-independent Proportional Hazard Model (PHM): a time-independent PHM has been employed according to the formulation as given in equation 1. Again, underlying distributions for the hazard function include the normal, log-normal, logistic, gamma, exponential and Weibull distributions. In essence, the GRP time-based reliability models described above are extended by introducing time-independent covariates. These covariates – represented as mean values over one flight - are taken from the MDA analysis, but are limited in number using forward selection to keep the standard error (and associated confidence intervals) within reasonable bounds. MLE is used again to perform parameter estimation.

3) Time-dependent Proportional Hazard model (PHM): Equation 1 is adjusted to take into account operational factors which vary over time; Z becomes Z(t). EVA analysis yields operational covariates, with values that can vary as a function of time. In time-dependent models the hazard rate for all flights related to a maintenance event is computed. Each observation (flight) is subject to some error. Ergo, it follows that the error of the computed reliability increases cumulatively. To limit the total error and omputational time, a

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forward selection approach has been implemented with a maximum of two iterations (two covariates).

3. Results

The method outlined in Section 2 is applicable to any component. In this study, results are derived from ten components with the highest impact in terms of unscheduled removal rate. Maintenance data with respect to these components was collected and spanned a period from 2004 – 2015. In addition, operational data was collected, spanning a period from 2011 – 2015. Table 1 gives some key characteristics of the dataset for one particular component (blade assembly and bearing), as well as output of the flight identification, EVA and MDA modules.

Table 1. Key dataset characteristics for blade assembly and bearing example

Dataset attribute Value

Number of components 1597 Number of flights (total) 548353 Number of operational variables (total) 1531 EVA output (relevant operational variables) 38 MDA output (relevant operational variables) 78

Figure 2 shows a visual example of the top operational factors influencing failures of blade assembly and bearing, following from MDA and EVA analysis. In the case of MDA analysis, a significant difference can be observed between the mean ambient pressure operating on failure event associated flights (as given in red) and the censored event associated flights (as given in blue). Similarly, for EVA analysis, the longitudinal acceleration (Accn_long_mean) is more severe for failure-associated flights when compared to flights associated with censored events (i.e., no failures).

Figure 2. Top operational factor as identified by MDA (left) and EVA (right)

In terms of effectiveness of time-based reliability models versus PHM variants, Table 2 shows MLE output, computational time and goodness-of-fit characteristics (according to the NRR test) for a subset of underlying distributions and varying number of operational factors. A few observations can be made. Firstly, the MLE estimator value is most optimal for time-dependent PHM, outperforming the other two model types. However, goodness-of-fit is best for time-independent PHM, with the additional observation that incorporation of additional operational factors increases accuracy of forecasts. For other components and other underlying distributions, these findings will vary, but in general, either time-independent or time-dependent PHM models will

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outperform time-based models, to various levels of accuracy. As mentioned, for the given example, the model accuracy increases when more operational factors are included into the analysis. This is however not a generalizable statement: the optimum number of operational factors will vary from component to component, and typically lies between two to five.

Table 2. Overview of GRP-II, time-independent and time-dependent PHM model results (MLE estimates;

NRR goodness-of-fit test; computational time) for underlying exponential distribution

GRP-II Time-independent PHM Time-dependent PHM

# oper. Factors N/A 1 2 3 4 1 2 3 4 MLE -643.6 -632.3 -624.5 -618.6 -614.7 -582.3 -502.0 -425.1 -387.1 NRR 44.45 37.32 32.43 35.41 27.21 104.25 76.37 46.38 60.14 time [min] << 1 6.35 8.7 11.34 13.51 46.1 89.7 152.2 217.9

4. Conclusions

Results derived from analysing and modelling the top 10 components, in terms of URRs, show that it is feasible to identify operational factors that have a significant influence on failure probability. The subsequent use of statistical models incorporating operational covariates (the time-dependent and time-independent versions of the Proportional Hazard Model) are suitable to incorporate the effects of these identified operational factors into reliability estimation. Results show that these models tend to outperform time-based models in terms of accuracy.

Limitations to this study are as follows. First, it is difficult to make an a priori assessment of which underlying distribution and which number of operational factors should be included into relability analysis. Analysis of historical data should be executed regularly to verify any choices with respect to these model settings. Furthermore, any reliability forecasts should be validated using a separate set of maintenance event data to be able to quantify the efficacy of the proposed method in terms of failures prevented and costs saved.

References

[1] IATA, 2014, Airline Maintenance Cost Executive Commentary - Exclusive Benchmark Analysis (FY2014 data), Accessed 20-01-2017. [Online]. Available:

https://www.iata.org/whatwedo/workgroups/Documents/MCTF/AMC-Exec-Comment-FY14.pdf

[2] D.M. Louit, R. Pascual and A.K.S. Jardine, A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliability Engineering & System Safety, Vol. 94(10), 2009, pp. 1618-1628. doi: http://dx.doi.org/10.1016/j.ress.2009.04.001

[3] S.E. Rigdon and A.P. Basu, Statistical methods for the reliability of repairable systems, Wiley, New York, 2000.

[4] M. Kijima and U. Sumita, A useful generalization of renewal theory: counting processes governed by non-negative markovian increments, Journal of Applied Probability, Vol. 23(1), 1986, pp. 71-88. [5] D.R. Cox, Regression models and life tables (with discussion), Journal of the Royal Statistical Society,

Series B, Vol. 34, 1972, pp. 187-220.

[6] A.K.S. Jardine and P.M. Andersen, Use of concomittant variables for reliability estimation, In:

Proceedings of the 8th_{symposium on Advances in Reliability Technology, Bradford University, 1984.} [7] A.K.S. Jardine and J.A. Buzacott, Equipment reliability and maintenance, European Journal of

Operational Research, Vol. 19(3), 1985, pp. 285-296.

[8] J.C. Nash, On best practice optimization methods in R, Journal of Statistical Software, Vol. 60(2), 2014, doi:http://dx.doi.org/10.18637/jss.v060.i02.

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A Multi-Criteria Decision Making

Framework for Aircraft Dispatch

Assessment

Hemmo KOORNNEEF1, Wim J.C. VERHAGEN and Richard CURRAN

Air Transport and Operations, Delft University of Technology

Abstract. The aircraft dispatch decision is a complex analysis based on many

factors related to airworthiness regulations, aircraft health status, resource availability at current and future stop(s) and operational preferences of the operator. Within the turnaround time (TAT) a decision has to be made whether the aircraft can return to service, defects have to be deferred, operational restrictions apply, maintenance has to be performed, or if the aircraft is unable to safely perform the next flight. This paper presents a framework for automated dispatch decision support and, as a first step of implementation of the framework, a proof of concept for automated root cause identification by means of a case study on a Airbus A320 wing anti-ice valve. A decision tree algorithm has been applied to a synthetic dataset, representing historical failure data with associated root causes and observed symptoms, achieving correct classification of the root cause for 40% of the cases. Analysis of the results show that the accuracy of the method increases with an increasing number of symptoms associated to a root cause. Furthermore, the method cannot distinguish between root causes with similar symptoms. Although the use of synthetic data restricts conclusions that can be drawn from the results, the work shows a proof of concept for automated root cause identification and leads to initial findings that are essential for future implementation and optimisation of the method. Eventually, the framework will be operationalised in the form of a mobile tool to assist stakeholders in on-site aircraft dispatch decision support.

Keywords. Aircraft maintenance, aircraft dispatch, decision support, multi-criteria

decision making, root cause analysis

Introduction

AoG, or aircraft on ground, is terminology used in aviation to indicate that a defect prevents the aircraft to continue scheduled flight operations. While AoG, the aircraft will not be generating revenue by transporting passengers or cargo. Therefore, airline operators aim to minimise AoG by carefully planning flight schedules and maintenance intervals. Because the regulated maintenance intervals are far from efficient with respect to remaining useful life (RUL), and because of the exponential growth of available data and computational power, the aviation industry is now moving towards more data-driven maintenance methods, like prognostics and health monitoring [1, 2]. Instead of performing maintenance at fixed time intervals, these methods use historical data and current health state to only perform maintenance when failure is expected in

1

Corresponding Author, Mail: H.Koornneef@tudelft.nl

Transdisciplinary Engineering: A Paradigm Shift C. Chen et al. (Eds.)

This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/978-1-61499-779-5-11

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the near future, thereby exploiting more of the RUL. These methods aim to prevent unexpected failures that lead to AoG, but are unlikely to entirely avoid unexpected failures for highly complex systems such as aircraft.

In current airline operations, unexpected failures remain a frequent cause for flight disruptions and should be resolved swiftly to minimise AoG and operational impact. Line maintenance deals with resolving unexpected failures and other maintenance activities within the flight schedule. To avoid delays, all maintenance should preferably be completed within the turn-around time (TAT), which is the time required to complete all ground handling and have the aircraft ready for the next departure [3]. While most line maintenance activities are planned and thus are prepared for, unscheduled tasks following from unexpected failures often disrupt the flight operation, because they require troubleshooting followed by an assessment of the aircraft’s capability to perform the next flight. This process is known as aircraft dispatch assessment. Based on the defect information an assessment has to be made if the aircraft can be 1) dispatched without restrictions; 2) dispatched with a component or system inoperative; 3) maintenance is required before dispatch, or that 4) the aircraft is incapable to perform the next flight. The aircraft dispatch assessment is a typical example of a Multi-Criteria Decision Making (MCDM) problem, having multiple alternatives and assessment criteria. In the MCDM domain many methods have been developed to assist the stakeholder to rank alternatives, and in general three main steps have to be performed [4]:

1. Determine relevant decision criteria and alternatives.

2. Attaching numerical measures to the relative importance (i.e., weights) of the criteria and to the impacts (i.e., the measures of performance) of the alternatives in terms of these criteria.

3. Process the numerical values in order to rank the alternatives.

However, determining the importance of a specific decision criterion for a given problem is hard, because the criterion itself can be difficult to assess due to lack of quantitative and reliable information (e.g., maintenance cost). Hence, to assess the stability of an optimal solution a sensitivity analysis of the decision criteria has to be performed in order for the MCDM method to be effective [5]. A stakeholder can make better decisions by knowing which criteria affect the solution the most. With many MCDM methods available, it is not straightforward to determine which method is most suitable for a specific problem. To assist in selecting the right MCDM method for a given problem the 11 most commonly used methods were analysed by Velasquez and Hester [6], listing their advantages and disadvantages. Multi-Attribute Utility Theory (MAUT) as an extension of Multi-Attribute Value Theory (MAVT), the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) are frequently used methods, of which the latter is widely used because of its simplicity. The general trend in recent years is to combine multiple methods to overcome specific weaknesses of a particular method. The exponential growth in computational power over the last decades has significantly accelerated this trend and also provides the opportunity to exploit large amounts of data for better decision making.

However, before any MCDM method can be applied to the aircraft dispatch assessment problem, the root cause of the defect needs to be identified first in order to determine the appropriate corrective maintenance action. Only then the different dispatch scenarios can be properly assessed. Current research on the aircraft dispatch problem assumes that the root cause for the defect is known [7], but for most cases the

H. Koornneef et al. / A MCDM Framework for Aircraft Dispatch Assessment

Advances in Transdisciplinary Engineering

Delft University of Technology

Advances in Transdisciplinary Engineering

TRANSDISCIPLINARY ENGINEERING: A PARADIGM

SHIFT

Advances in Transdisciplinary Engineering

Volume 5

Transdisciplinary Engineering:

A Paradigm Shift

Proceedings of the 24th ISPE Inc. International Conference on

Transdisciplinary Engineering, July 10–14, 2017

Edited by

Chun-Hsien Chen

Nanyang Technological University, Singapore

Amy C. Trappey

National Tsing Hua University, Taiwan

Margherita Peruzzini

University of Modena and Reggio Emilia, Italy

Josip Stjepandić

PROSTEP AG, Germany

and

Nel Wognum

TU Delft, The Netherlands

Preface

Committees

Contents

Part 1

Air Transport and Traffic Operations and

Management

Component-Based Data-Driven Predictive

Maintenance to Reduce Unscheduled

Maintenance Events

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A Multi-Criteria Decision Making

Framework for Aircraft Dispatch

Assessment