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Specialization: Transport Engineering and Logistics
Report number: 2014.TEL.7857
Title:
Optimization of the tram wheelmaintenance planning for the RET
Author:
M.B. Erkens
Title (in Dutch): Optimalisatie van de planning van onderhoud van de tramwielen voor de RET
Assignment: Master’s thesis
Confidential: no
Initiator (university): prof.dr.ir. G. Lodewijks
Initiator (company): G.A.M. van Rooijen (RET, Rotterdam) Supervisor: dr. ir. Y. Pang
Delft University of Technology Department Maritime and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Student: M.B. Erkens Assignment type: Master’s thesis
Supervisor (TUD): dr. ir. Y. Pang Creditpoints (EC): 35 Supervisor (RET): G.A.M. van Rooijen Specialization: TEL
Report number: 2014.TEL.7857 Confidential: no
Subject: Optimization of the tram wheel maintenance planning for the RET
Company
The RET, Rotterdamse Elektrische Tram, is the main public transport operator in Rotterdam, the Netherlands. The RET operates different types of public transportation, using 38 bus lines, 9 tramlines and 8 metro lines. This is realized with a fleet of 238 buses, 113 trams and 152 metro trains. Since 2008 the RET also transports passengers over water, using the Fast Ferry.
This assignment focusses on one of the transportation types of the RET, the trams. The Rotterdam tramway network consists of 194 kilometer of rails, 443 switches and 322 stops. All 113 trams are stored during the night in one of two depots: Beverwaard and Kralingen. The trams that are currently used are fabricated by Alstom in France, they are called the Citadis I and Citadis II. The trams of type Citadis I were delivered between 2003 and 2004. The Citadis II between 2009 and 2012. All the older trams that were used before these two types are no longer in full operation.
Reason for the assignment
A tram wheel consists of a horizontal part which carries the weight of the tram and a flange which guides the wheels along the rails. Because the contact between the rail and wheel is steel onto steel,
significant amounts of wear occur on both the horizontal part and the flange. In order to keep the
wheels of a tram in proper shape for operation, the wheels have to be serviced from time to time and replaced when necessary. The total costs of the maintenance of the tram wheels are the largest expense on the maintenance of the trams at the RET and a relative small reduction in the total maintenance of the tram wheels can already provide significant savings in maintenance costs.
In order to get a better view of the wear of the wheels of each tram a measuring system was installed at each depot. This system measures multiple properties of each tram wheel, such as diameter, flange thickness, flange height and flange slope. These properties can be used to determine whether a wheel
is still good or has to be turned to restore the wheel profile or even replaced at one of the depots. However, the data from these measurements has to be analyzed first and in the current situation this is done using MATLAB and Microsoft Excel. This still requires a lot of manual labor which takes a lot of time and therefore costs a lot of money. Also in the current situation the data is only used to determine the current state of the wheels. In order to determine the wear rate, the amount of wear is only compared to the period of time that has passed, the travelled distance is currently not taken into account. The wear of the wheels should be linked to the travelled distance of the trams, in order to compare different trams and determine the actual wear rate of a tram. If the data from the wear measurements is combined with other data of the trams, such as actual service hours, actual travelled distance or many other variables, the causes off differences in wear between wheels, trams or even tramlines can be determined more accurately. This information can be used to find and minimize causes of excessive wear in order to bring the wear rates of all trams to a uniform level as low as possible. All these parameters can also be used to make a forecast on the wear of the tram wheels in order to plan maintenance activities on the wheels, such as turning or replacement. The desired situation is to turn or replace all wheels as close to the end of their lifetime as possible, in order to minimize the waste in capacity.
The overall goal of the RET is to reduce the wear of the wheels of all trams to a uniform low level in order to reduce the total costs.
Aim of this assignment
This assignment is intended to create an automated system to monitor the wear of the tram wheels, provide a forecast on the future wear and to automate the planning of the tram wheel maintenance. In order to accomplish this, there are a number of questions that need to be answered:
1. What is the reliability of the measurements and what part are measurement errors?
2. What are the criteria for the decision for service or replacement of a wheel and how can these be determined from the measured data?
3. How can the data from the measurements be automatically filtered on measuring errors, and how can the correct data be used to determine the actual wear rates of the tram wheels? In this part there are more difficulties with the data that should be taken care of automatically, such as the turned or replaced wheels.
4. How can the filtered data be used together with the specified criteria from 2) in order to make planning decisions for servicing or replacing of the wheels?
5. How can the future wear of a tram wheel be predicted for planning purposes?
6. How can the entire maintenance planning be automated based on the analyzed measurement data?
Approach
The measuring system has been operational for a substantial period of time already (about one and a half year), so a large set of rough data is available. Because an automated system has to be developed which can provide information on the current state of the tram wheels but also make a forecast, not the entire dataset should be used to analyze the data and create the model. First a part of the dataset should be used to answer the six questions described at the Aim of the assignment. The answers on these questions can be used to create a model to predict the wear of the tram wheels and this model can be verified by comparing the prediction of the model with the actual wear of the tram wheels in a next part of the dataset. If the model provides satisfactory results the model can be used, otherwise the model parameters have to be optimized and this step must be repeated.
Supervision Gerard van Rooijen
Sr. technical specialist | Vehicle engineering department | RET
Time frame
This assignment should be carried out during a period of 26 weeks, starting 19-5-2014.
The report should comply with the guidelines of the section. Details can be found on the website.
The professor,
I
Preface
This is a thesis for the Master study Mechanical Engineering with the track Transportation Engineering and Logistics at the Technical University in Delft. The assignment to automate and optimize the maintenance planning for the tram wheels based on available measurement data has been provided by the RET located in Rotterdam.
I would like to thank Mr. G.A.M. van Rooijen and Mr. L. Koot from the RET and dr.ir. Y. Pang and prof.dr.ir. G. Lodewijks from the Technical University in Delft for their supervision and support during this assignment.
II
Summary
The RET, or Rotterdamse Elektrische Tram, is the main public transport operator in the city of Rotterdam and they use buses, metro trains and trams. This thesis focusses on one of the transportation types of the RET, the trams. The Rotterdam tramway network consists of 194 kilometer of rails, 443 switches and 322 stops. During the night, all 128 trams are stored in one of two depots, one located at Beverwaard and one located at Kralingen.
Maintenance of the tram wheels is a very large expense for the RET, mainly because both the rails and the tram wheels are made of steel, which causes a lot of wear. Wear causes the shape of the wheel profile to change, which can lead to a number of inconveniences. When the wheel profile shape is not optimal, this affects the both the safety and the performance of the trams along with the comfort of the passengers. Especially for safety issues, there are certain limits for the tram wheels that indicate whether a tram is still allowed to be used or not. If a tram does not comply with these regulations, the wheels of this tram are turned and the shape of the wheel profile is restored if possible. If the wheel cannot be turned anymore the wheel tire is replaced by a new wheel tire. The RET wants to save money on the maintenance of the tram wheels, but also wants to raise the overall quality of the wheels of the trams in operation. To achieve this, the RET has purchased two measuring systems for the tram wheels and installed these at both depots. When a tram passes one of these measuring systems, it automatically measures multiple parameters on each tram wheel, which are used to determine the shape of the wheel profile.
In the current situation the measuring systems are operational and every tram is measured frequently. This measurement data is analyzed manually in order to provide information to help make the planning for the wheel maintenance. The use of the measuring systems has improved the overall state of the wheels of the entire tram fleet, but according to the RET this is still far from the optimal situation. The accuracy of the measuring system is not as high as was counted on, due to a large number of disturbances and measurement errors. This makes the interpretation of the measurement data much more difficult and time consuming. Therefore only a few of the measured wheel parameters are analyzed and this analysis is only performed once a month. Due to the fact that the analysis of the measurement data is only performed partially and monthly, there are a lot of trams in operation which do not comply with the regulations and there are large differences between the states of different trams in the fleet.
In order to further improve the overall state of the wheels of the entire tram fleet, the analysis of the data of the measuring system must be optimized. This requires a number of alterations in the current process from measurement data to maintenance planning. The scope of this thesis is the entire process that translates the measurement data into a planning for the tram wheel maintenance. There are a lot of improvements possible in this process to get a better view of the state of the wheels of different trams, which can help optimize the maintenance planning.
III A method is proposed to improve analysis of the data from the wheel tire measuring systems installed at the depots at Beverwaard and Kralingen, in order to enable automated decision making and wear predictions. This has been achieved by implementing a newly designed step recognition algorithm and the robust locally weighted regression method. The step recognition algorithm detects discrete steps in the measurement data, which are caused by maintenance actions on the wheel tires, such as replacing or turning of the wheel tires, and divides the data into sections separated by these discrete steps. This method has been tested and verified using historic measurement data. Using the algorithm to divide the datasets into separate sections has made it possible to use the robust locally weighted regression method to further filter and smooth the separated sections of the datasets. The combination of these two methods has resulted in much more reliably processed data which can be used for the automated decision making and predictions of the wear of the wheel tires.
For the automation of the maintenance decision making a fault tree has been implemented. This fault tree is used to check on all defined criteria to which the wheels of a tram must comply in order to determine which trams need maintenance. This is an improvement on the current manually performed criteria check process because it improved the reliability, saves time and enables more frequent analysis of the data and now all the defined criteria are checked. Another improvement is the developed method for the prediction of the wear based on the measurement data. For this the existing warning limits for all wheel parameters have been altered in order to guarantee a remaining time before the rejection limit is reached of at least one month. When a warning limit is crossed by one of the wheel parameters, a prediction is made of when this parameter will reach the rejection limit. This prediction is based on extrapolation of the available data and a linear trend prediction. The prediction method has been tested on the application of the wheel tire parameters and it can be concluded that it provides a sufficiently reliable prediction of the wear of the tram wheels for a prediction length of one month and that the method is suitable for the application of the wheel tires of the tram of the RET.
Finally the analyzed measurement data and the results from the automated decision making and predictions are used to determine the type of maintenance action a tram needs and to estimate the needed time for this process. This has been realized by analyzing the shapes of new and worn wheel profiles and by using the available information from the maintenance department. Of every tram which needs maintenance it can be determined which wheels need maintenance, before which date this maintenance should be performed, what type of maintenance action is required on each bogie of the tram and long this maintenance will take. In order to investigate the possibility of automating the maintenance planning based on this information, the maintenance facilities and current planning process has been analyzed. During this analysis a large number of complications with the maintenance planning were encountered, such as the availability of operators to perform the maintenance activities, the availability of the right trams for wheel maintenance and unplanned maintenance activities.
IV
Based on the findings in this thesis it can be concluded that it is possible to improve the current maintenance planning, based on the measurement data of the wheel tire measuring systems. Good preparation of the data, by means of proper filtering and smoothing, enables the automation of the maintenance decision making process and the extension of this process with a prediction of the maintenance requirements of the trams for the length of a month. The analyzed measurement data can also be used to obtain accurate information on the required maintenance activity on the tram wheels and the expected time needed for this particular maintenance. After analysis of the current maintenance planning process and the facilities of the maintenance department it has been concluded that it is not possible to automate and optimize the maintenance planning any further. If an optimal and automatically created planning is required for the maintenance of the tram wheel tires more research is necessary in order to link this planning to the planning of the maintenance of other parts of the trams, major overhaul and the management of the trams used in operation. The obtained information from the measurement data in this thesis is the best possible solution for the current situation of the tram wheel tire maintenance planning and this will be used in practice by the RET for the planning of their wheel tire maintenance.
There are a number of recommendations provided in this thesis. First of all the measuring systems need to be investigated, because the maintenance decision making and the wear prediction in this thesis is mainly limited by the quality of the measurement data. The measurement data contains a lot of noise and large peaks and the data of both measuring systems cannot be combined due to calibration differences. In order to improve the results from the automated system, designed in this thesis, first an improvement of the measuring systems is required.
To enable automation of the entire planning of the wheel tire maintenance, a number of other factors need to be further investigated. First of all the planning of the operators needs to be optimized in order to know the exact capacity of the different parts of the maintenance department. The next step is to analyze the historic data more accurately on the occurrence of unplanned maintenance to take this into account in the maintenance planning. In this analysis also the seasonal dependence needs to be taken into account. Finally the planning of the maintenance of the tram wheels needs to be linked to the planning of the trams in operation and the planning of the other types of maintenance on the trams. If all this can be realized it may be possible to automate the entire maintenance planning for the tram wheel tires.
With the methods in this theses the measurement data has been analyzed, which has enabled the RET to analyze the available historic data, because it is filtered and smoothed correctly. This makes it possible to analyze the differences in wear between different trams, bogies, tram types and tram routes for example. This can help in getting more insight in the causes of excessive wear and how to prevent this. This can help the RET to improve the overall quality of the wheel tires of all trams and save money.
V
Summary (in Dutch)
De RET, of Rotterdamse Elektrische Tram, is het grootste openbaar vervoersbedrijf in Rotterdam en exploiteert de bussen, metro´s en trams. Dit afstudeeronderzoek richt zich op één van deze transportmiddelen, namelijk de trams. Het tramnetwerk in Rotterdam bestaat uit 194 kilometers rails, 443 wissels en 322 haltes. Gedurende de nacht staan alle 128 trams gestald in één van de twee remises van de RET in Beverwaard en Kralingen.
Het onderhoud van de tramwielen is de grootse kostenpost voor de RET en dit komt vooral doordat zowel de rails als de tramwielen van staal zijn, wat veel slijtage tot gevolg heeft. Slijtage resulteert in een verandering van de vorm van het wielprofiel, wat kan leiden tot verscheidene ongemakken. Als het wielprofiel niet meer optimaal van vorm is, beïnvloedt dit de veiligheid, de prestaties van de tram, het comfort van de passagiers. Vooral voor de veiligheid zijn er eisen gesteld voor de tram wielen, die aangeven of een tram nog gebruikt mag worden of niet. Als de wielen van een tram niet aan deze eisen voldoen, worden deze wielen gedraaid waardoor het wielprofiel weer hersteld kan worden. Als een wielband niet meer gedraaid kan worden, wordt deze vervangen door een nieuw wielband. Het doel van de RET is om meer geld te besparen op het onderhoud van de tramwielen, maar ook om de staat van de wielen van alle trams naar een gelijk, en hoger, niveau te brengen. Om dit te bereiken heeft de RET twee wielmeetsystemen aangeschaft en bij beide remises er één geïnstalleerd. Als een tram over één van deze twee wielmeetsystemen rijdt worden meerdere parameters van elk wiel gemeten, welke worden gebruikt om de vorm van het wielprofiel te bepalen.
In de huidige situatie zijn beide wielmeetsystemen volledig operationeel en worden alle trams frequent gemeten. De meetdata wordt handmatig geanalyseerd om informatie te leveren aan de onderhoudsafdeling. Sinds de aanschaf van wielmeetsystemen is de algehele staat van de tram wielen al flink gestegen, maar volgens de RET is dit nog lang niet de optimale situatie. De nauwkeurigheid van de meetsystemen is minder dan waar van tevoren op was gerekend. De oorzaak hiervan is een groot aantal meetfouten en verstoringen in de meetdata. Dit leidt ertoe dat het analyseren van de meetdata erg moeilijk en tijdrovend is. Om die reden worden niet alle wielparameters die gemeten worden geanalyseerd en wordt deze analyse maar één keer per maand gedaan. Doordat de meetdata maar één keer per maand en niet alle wielparameters geanalyseerd wordt, zijn er een groot aantal trams waarvan de wielen niet voldoen aan alle eisen en zijn er grote verschillen tussen de trams.
Om de algehele staat van alle tramwielen verder te verbeteren moet de analyse van de meetgegevens worden geoptimaliseerd. Hiervoor zijn een aantal aanpassingen nodig aan het gehele proces vanaf meetdata tot de onderhoudsplanning. De scope van dit onderzoek is het hele proces wat de meetgegevens omzet naar een optimale planning voor het onderhoud van de tramwielen. Er zijn verschillende verbeteringen mogelijk die het mogelijk maken een beter beeld te krijgen van de staat
VI
van de wielen van de verschillende trams, wat kan helpen bij het optimaliseren van de onderhoudsplanning.
In dit onderzoek is een methode geïntroduceerd voor het verbeteren van de analyse van de meetgegevens van de wielmeetsystemen, om het automatiseren van onderhoudsbeslissingen mogelijk te maken. Dit is gerealiseerd met het ontwerp en de implementatie van een nieuw stapherkenningsalgoritme en met de robuuste versie van de locally weighted regression methode. Het stapherkenningsalgoritme herkent discrete stappen in de meetgegevens, welke veroorzaakt worden door onderhoudsactiviteiten zoals het draaien of vervangen van de tramwielbanden. De datasets worden door het algoritme in secties verdeeld, tussen twee onderhoudsacties. De ontwikkelde methode is getest met behulp van historische meetdata. Het gebruik van het algoritme heeft het mogelijk gemaakt om de robuuste locally weighted regression methode toe te passen op de losse secties van de datasets voor verdere filtering en smoothing van de data. De combinatie van deze twee methodes heeft geresulteerd in een hogere betrouwbaarheid van de data die gebruikt kan worden voor het automatiseren van de onderhoudsbeslissingen voor de wielonderhoud en het voorspellen van de slijtage in de toekomst.
Voor het automatiseren van de onderhoudsbeslissingen is er een foutenboom, ofwel fault tree, geïmplementeerd. Deze fault tree wordt gebruikt om alle gedefinieerde criteria waar de wielen van een tram aan moeten voldoen te controleren en te bepalen welke trams wielonderhoud nodig hebben. Dit is een verbetering op het huidige handmatige analyse proces, omdat hiermee de betrouwbaarheid omhoog gaat, er veel tijd mee wordt bespaard en een meer frequente analyse van de tramwielen mogelijk gemaakt wordt. Een andere verbetering is de ontwikkeling en implementatie van een voorspellingsmethode gebaseerd op recente meetdata. Hiervoor zijn de waarschuwingslimieten voor alle wielparameters zodanig aangepast dat er gegarandeerd kan worden dat elke tram nog minimaal een maand kan rijden voordat er een afkeur limiet bereikt wordt. Wanneer één van de wielparameters een waarschuwingslimiet bereikt, wordt er een voorspelling gemaakt wanneer deze wielparameter de afkeurlimiet zal bereiken. Deze voorspelling is gebaseerd op een extrapolatie van de beschikbare recente meetdata en een volledig lineaire voorspelling. Deze voorspellingsmethode is getest voor de toepassing van de wielbanden van de trams en er is geconcludeerd dat deze voldoende betrouwbare voorspelling genereert voor de tramwielbanden voor de lengte van een maand. Hiermee is de methode geschikt voor gebruik voor de RET.
Ten slotte zijn de geanalyseerde meetdata en de resultaten van de geautomatiseerde onderhoudsbeslissingen gebruikt voor het automatisch bepalen van het type onderhoudsactie wat de wielen van een tram nodig hebben, draaien of vervangen, en hoe lang dit onderhoud zal gaan duren. Om dit te bereiken is de vorm van het tramwielprofiel, nieuw en versleten, geanalyseerd en informatie van de onderhoudsafdeling zelf gebruikt. Van elke tram kan het nu bepaald worden welke wielen er onderhoud nodig hebben, wat de uiterste datum voor dit onderhoud is, welke type onderhoudsactie
VII nodig is en hoe lang dit onderhoud zal gaan duren. Om te onderzoeken of het mogelijk is om de volledige onderhoudsplanning te automatiseren op basis van deze gegevens, zijn eerst de onderhoudsfaciliteiten en het proces van de huidige onderhoudsplanning geanalyseerd. Tijdens deze analyse zijn een aantal complicaties aan het licht gekomen, zoals het feit dat er niet altijd monteurs beschikbaar zijn voor het uitvoeren van het wielonderhoud, het feit dat de juiste trams vaak niet beschikbaar zijn voor onderhoud en ongeplande onderhoudsactiviteiten buiten de planning om.
Op basis van de bevindingen in dit afstudeeronderzoek kan worden geconcludeerd dat het mogelijk is om de huidige onderhoudsplanning de verbeteren op basis van de meetgegevens van de wielmeetsystemen. Goede verwerking van de meetdata, door middel van filteren en smoothen, maakt het mogelijk om de onderhoudsbeslissingen te automatiseren. Ook is dit proces uitgebreid met een voorspelling van de wielslijtage voor de periode van een maand en kan er automatisch bepaald worden wat voor type onderhoud de wielen van een tram nodig hebben. Na analyse van het huidige planningsproces en de onderhoudsfaciliteiten kan er worden geconcludeerd dat het momenteel niet mogelijk is om de onderhoudsplanning verder te automatiseren en optimaliseren. Als een volledig geautomatiseerde onderhoudsplanning gerealiseerd moet worden, is het vereist om een koppeling te creëren tussen de planning van het wielonderhoud, de planning van ander tramonderhoud zoals grote revisies en de planning van de tram die in dienst beruikt worden. De verkregen informatie uit de meetdata van de wielmeetsystemen in dit afstudeeronderzoek is de beste oplossingen in de huidige situatie van de planning van het onderhoud van de tramwielen en dit zal ook direct toegepast worden door de RET.
Er zijn een aantal aanbevelingen op basis van dit afstudeeronderzoek. Allereerst moeten de meetsystemen zelf worden onderzocht, omdat de onderhoudsbeslissingen en de slijtagevoorspellingen momenteel vooral gelimiteerd worden door de kwaliteit van de meetdata. The meetdata bevat veel ruis en grote pieken en de meetgegevens van beide wielmeetsystemen kan niet samengevoegd worden door kalibratieverschillen. Om de resultaten van het geautomatiseerde systeem, ontworpen in dit onderzoek, te verbeteren is het eerst nodig om de meetsystemen zelf te verbeteren.
Voor volledige automatisering van de onderhoudsplanning moeten er een aantal zaken verder onderzocht worden. Allereerst moet de planning van de monteurs verbeterd worden om een gegarandeerde capaciteit te creëren voor het wielonderhoud. Daarnaast kan de historische meetdata geanalyseerd worden om te bepalen hoeveel invloed ongeplande onderhoudsactiviteiten hebben op de onderhoudsplanning om hiervoor een veiligheidsmarge in te voeren. Hierbij moet ook de invloed van de jaargetijden meegenomen worden. Als laatste moet er een koppeling gecreëerd worden tussen de planningen van de verschillende onderhoud types van de trams en de dienstregeling. Als dit allemaal gerealiseerd is, is het mogelijk om de volledige onderhoudsplanning te automatiseren.
VIII
De in dit onderzoek gebruikte methoden hebben het mogelijk gemaakt voor de RET om de historische meetdata te analyseren, omdat deze nu goed gefilterd en gesmoothed is. Dit maakt het mogelijk om de verschillen in slijtage tussen trams, draaistellen, tram types en tram routes te analyseren en de oorzaken hiervan te bepalen. Dit kan de RET gebruiken om de algehele staat van de tramwielen de verbeteren en mogelijk veel geld te besparen.
IX
List of symbols
a [mm] Allowed diameter difference wheel on same axle
b [mm] Allowed diameter difference wheel on same side of a bogie (bogie 1 and 3) c [mm] Allowed diameter difference wheel on same side of a bogie (bogie 2)
C1 [mm] Warning limit
C2 [mm] Rejection limit
d [mm] Allowed average diameter difference bogie 1 and 3 D1 [mm] Diameter of right wheel
D2 [mm] Diameter of left wheel
dday [km] Average travelled distance a day per tram ddmax [mm] Maximal diameter removed each turning cycle Dmin [mm] Minimal diameter size for wheel lathe
E Allowed number of consecutive errors
e Number of consecutive errors
E1 [mm] Wheel tire thickness right wheel E2 [mm] Wheel tire thickness left wheel eb1 [mm] Flange thickness right wheel eb2 [mm] Flange thickness left wheel
ebmax [mm] Maximal flange thickness after turning
ebmin,even [mm] Minimal flange thickness after turning for even numbered wheels ebmin,odd [mm] Minimal flange thickness after turning for odd numbered wheels
Ei [mm] Wheel gauge
Eprediction,abs [km] Absolute prediction error
Eprediction,early [km] Prediction error of early predictions Eprediction,late [km] Prediction error of late predictions hb1 [mm] Flange height right wheel
hb2 [mm] Flange height left wheel
L Minimal section length
m Median of absolute residuals of locally weighted regression fit
Nearly Number of too early predictions
Nlate Number of too late predictions P [km] Predicted mileage left
P1 [mm] Hollow wear right wheel
p1 Linear polynomial coefficient
P2 [mm] Hollow wear left wheel
p2 Quadratic polynomial coefficient
plate [%] Percentage of too late predictions
Pwrong [%] Percentage of mistakes step recognition algorithm qR1 [mm] Flange steepness factor right wheel
qR2 [mm] Flange steepness factor left wheel Ri [mm] Rejection limit wheel parameter i
S1 Rejection state
S2 Warning state
Saxle,i State of axle i
Sbogie,i State of bogie i
sf Safety factor prediction
X
T [mm] Threshold
tadditional [min] Process time additional per bogie tend [days] Date of last measurement tp [days] Predicted rejection date tturn [min] Process time turning cycle
W Threshold width
Wi [mm] Warning limit wheel parameter i
X Rough input dataset
y Rough dataset for locally weighted regression fit ŷ Estimated fit of locally weighted regression
Yn Filtered output section n
ΔD [mm] Decrease in wheel diameter by turning
ΔDmax [mm] Maximum decrease in wheel diameter by turning ε Residuals of locally weighted regression fit
XI
List of abbreviations
ANN Artificial neural network
ARIMA Autoregressive integrated moving average
BVW Depot located at Beverwaard
CUSUM Cumulative sum control chart
EVA Artificial Vision Wheelset Parameter Equipment
KRL Depot located at Kralingen
MCDM Multi criteria decision making
RET Rotterdamsche Elektrische Tram
SSE Sum of squared errors
WMS Wheel measuring system
XII
Contents
Preface ... I Summary ... II Summary (in Dutch) ... V List of symbols ... IX List of abbreviations ... XI Contents ... XII 1 Introduction ... 1 2 Background ... 4 2.1 RET organization ... 4 2.2 Tram configuration ... 4
2.3 Tram wheel profile ... 5
2.4 Maintenance of the tram wheel tires ... 6
2.5 Measuring systems ... 8
2.6 Maintenance criteria ... 9
2.7 Current analysis process... 10
2.8 Problem definition ... 12 2.9 Scope ... 12 2.10 Research questions ... 13 2.11 Approach ... 13 3 Data analysis ... 15 3.1 Problem ... 15 3.2 Methodologies ... 17 3.2.1 Existing methods ... 17 3.2.2 Step recognition ... 19
3.2.3 Filtering and smoothing methods ... 24
3.3 Implementation ... 26
3.3.1 Synchronization of steps for one bogie ... 26
3.3.2 Tuning and testing of the step recognition algorithm ... 28
3.3.3 Smooth dataset sections ... 35
3.3.4 Results ... 35
XIII 4 Maintenance decisions ... 37 4.1 Problems ... 37 4.2 Methodologies ... 38 4.2.1 Criteria check ... 38 4.2.2 Maintenance priority ... 39 4.3 Implementation ... 46 4.3.1 Criteria check ... 46 4.3.2 Maintenance priority ... 47 4.4 Conclusion ... 54 5 Maintenance planning ... 56 5.1 Maintenance process... 56 5.1.1 Turning ... 56 5.1.2 Replacing ... 58
5.2 Maintenance planning information ... 59
5.2.1 Decision maintenance activity ... 59
5.2.2 Turning process time ... 60
5.3 Implementation ... 62
5.3.1 Problems ... 62
5.3.2 Results ... 63
5.3.3 Testing ... 64
5.4 Optimal planning ... 65
5.4.1 Facilities and capacity ... 66
5.4.2 Complications ... 66
5.5 Conclusion ... 67
6 Conclusions ... 69
7 Recommendations ... 72
References ... 73
Appendix A Scientific Research Paper ... 75
Appendix B Measuring system specifications ... 84
Appendix C Examples of the influence of the step recognition parameters ... 87
Appendix D List of maintenance activities from maintenance department. ... 89
XIV
Appendix F Reference material for verification step recognition algorithm ... 97
Appendix G Optimization of step recognition algorithm: narrow parameter ranges ... 98
Appendix H Optimization step recognition algorithm ... 100
Appendix I Examples of wrong detections by the step recognition algorithm ... 101
Appendix J Analysis historic measurement data ... 103
1
1 Introduction
This Master Assignment focusses on the automation and optimization of the planning of the maintenance of the tram wheel tires at the RET in Rotterdam, based on the measurement data from existing measurement systems.
The RET, or Rotterdamse Elektrische Tram, is the main public transport operator in the city of Rotterdam and they use buses, metro trains and trams. This thesis focusses on one of the transportation types of the RET, the trams. The Rotterdam tramway network consists of 194 kilometer of rails, 443 switches and 322 stops. During the night, all 128 trams are stored in one of two depots, one located at Beverwaard and one located at Kralingen.
Both the rails and the tram wheels are made of steel, which causes much wear. Wear causes the shape of the wheel profile to change, which can lead to a number of inconveniences. When the wheel profile shape is not optimal, this affects the both the safety and the performance of the trams along with the comfort of the passengers. Especially for safety issues, there are certain limits for the wheels of a tram that indicate whether this tram is still allowed to be used or not. If a tram does not comply with these regulations, the wheels of this tram receive maintenance and the shape of the wheel profile is restored. In the past the RET used a preventive maintenance strategy (Raymond & Joan, 1991), which means the trams visit the maintenance department periodically. There is also the possibility a tram gets maintenance because there have been complaints from tram drivers, passengers or people who live near tram routes on excessive noise or inconveniences caused by the behavior of a tram. This can considered to be corrective maintenance (David & Arthur, 1989). In 2010 the total costs for the maintenance of the trams was €6,900,000 and the costs for the maintenance of the tram wheels alone was €900,000, which is 13 % of the total. The RET wants to save money on the maintenance of the tram wheels, but also wants to raise the overall quality of the wheels of the trams in operation. To achieve this, the RET has purchased two measuring systems for the tram wheels and installed these at both depots. When a tram passes one of these measuring systems, it automatically measures multiple parameters on each tram wheel, which are used to determine the shape of the wheel profile. This enabled the RET to change their maintenance strategy from preventive maintenance to condition based maintenance (Kelly & Harris, 1978).
In the current situation the measuring systems are operational and every tram is measured frequently. This measurement data is analyzed manually in order to provide information to help make the planning for the wheel maintenance. The use of the measuring systems has improved the overall state of the wheels of the entire tram fleet, but according to the RET this is still far from the optimal situation. The accuracy of the measuring system is not as high as was counted on, due to a large number of disturbances and measurement errors. This makes the interpretation of the measurement data much
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more difficult and time consuming. Therefore only a few of the measured wheel parameters are analyzed and this analysis is only performed once a month. Due to the fact that the analysis of the measurement data is only performed partially and monthly, there are a lot of trams in operation which do not comply with the regulations and there are large differences between the states of different trams in the fleet.
In order to further improve the overall state of the wheels of the entire tram fleet, the analysis of the data of the measuring system must be optimized. This requires a number of alterations in the current process from measurement data to maintenance planning. The scope of this thesis is the entire process that translates the measurement data into a planning for the tram wheel maintenance. There are a lot of improvements possible in this process to get a better view of the state of the wheels of different trams, which can help optimize the maintenance planning.
The main goal in this thesis is to optimize the planning of the maintenance of the tram wheel tires, based on the wheel measurement data, which has led to the main research question:
“How can the current tram wheel maintenance planning be optimized based on measurements of the tires of the tram wheels?”
Because the optimization of the maintenance planning can be realized by a number of different improvements in the current analysis process, five sub-questions have been composed:
1. How can the currently available data from the wheel tire measuring systems be analyzed in order to enable automated decision making and wear predictions?
2. How can the measurement data be analyzed automatically to determine of which trams the wheel tires need maintenance?
3. How can the analyzed measurement data be used to make predictions for the wear of the tram wheels to help optimize the planning?
4. How can the measurement data provide information on the type of maintenance activity that is needed and the expected time this will take?
5. How can all the obtained information from the measurement data be used to automatically generate an optimal maintenance planning for the tram wheels?
First in chapter 2 the needed background information will be provided and the problem investigated in this thesis will described in more detail. Then in chapter 3 the first sub-question will be answered and a method will be proposed and implemented to enhance the quality of the analysis of the data generated by the measuring systems to make it suitable for automated analysis. In chapter 4 the second and third sub-question will be answered and methods will be provided to determine which trams need maintenance and to make predictions for the tram wheel wear. This information is needed for the
3 planning of the tram wheel maintenance and the proposed methods will also be implemented in the application of the tram wheel measurement data. Next in chapter 5 the last two sub-questions are evaluated. First the measurement data, wheel characteristics and maintenance processes are analyzed in order to find a method to determine which type of maintenance action is required on a bogie and to estimate the expected time needed for this maintenance action. Finally the possibility of automation of the entire planning of the maintenance of the tram wheel tires is investigated.
Finally the conclusions of the research of this thesis will be discussed and a number of recommendations are provided based on the findings during this research and in Appendix A a scientific paper of this research is added.
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2 Background
2.1
RET organization
The RET is the main public transport operator in the city of Rotterdam. The RET operates the buses, metro trains and trams in the entire city of Rotterdam. In 2012 a total of 145 million passengers travelled with the RET on either bus, metro train or tram, together travelling 678 million kilometers (RET, 2013). This thesis only focusses on the trams, which in 2012 transported 41 million passengers over a total of 121 million kilometers. The RET also takes care of the transportation network itself, such as the rails and the stations. In order to show the business structure of the RET, a simple organogram is shown in Figure 1.
This research is performed for the technical department, specifically the fleet management department (Vloot Management). This department handles all technical problems on the vehicles used by the RET, which also includes the maintenance of the vehicles.
2.2
Tram configuration
The Rotterdam tramway network consists of 194 kilometer of rails, 443 switches and 322 stops. During the night, all 128 trams are stored in one of two depots, one located at Beverwaard and one located at Kralingen. The RET owns three types of trams for the transport of their passengers. The oldest type is the Zwevend Gelede Tram (ZGT), delivered between 1981 and 1985. Later in 2003 the RET started to use the 60 newly purchased Citadis trams, produced by Alstom in France. The advantage of these trams compared to the ZGT is that they have a low(ered) floor, which makes the getting on and off the tram easier. In 2011 the RET started using the newest generation of trams by Alstom, the Citadis II. They ordered 53 new trams to replace all the ZGT trams that were still in use. During regular operation,
5 nowadays only the Citadis I and Citadis II trams are used. The RET still kept 15 ZGT trams as back-up material. In Figure 2 simple views of the three trams types are shown.
Because the ZGT trams are not used during normal operation, these are beyond the scope of this research. The Citadis trams of type I and II are not identical, but the differences do not affect the scope of this research. All Citadis trams have 5 body sections supported by three bogies and connected by flexible joints, as shown in Figure 3. Each tram has 12 wheels, 4 on each bogie. The odd numbered bogies are powered, the middle one is only for supporting the tram and guiding it along the track. The powered bogies have separate drivelines to power the two wheels on the left and the two wheels on the right side.
Figure 3: Wheel and bogie configuration Citadis I and Citadis II
2.3
Tram wheel profile
The tram wheels are used to carry the weight of the tram, but also to guide the tram along the curves in the track. The part of the wheel which carries the weight of the tram is called the wheel thread. This part of the wheel is slightly conical, in order to keep the tram running in the center of the track. The wheel also has a part with a larger diameter, called the flange, which is the part that guides the wheels along the rails, especially in curves. In Figure 4 the configuration of the wheels on the rails is shown schematically.
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A tram wheel is not a solid piece of steel. A wheel consists of a steel core and a steel tire with rubber blocks in between them. A drawing of a tram wheel and its cross section are shown in Figure 5. In this drawing the black chevron shaped objects are the rubber blocks, fitted between the wheel core and the tire. These rubber blocks are used for damping in the wheel for comfort and for the reduction of noise caused by the tram wheels. When the profile of a wheel is worn out, only the tire has to be replaced and not the entire wheel.
2.4
Maintenance of the tram wheel tires
Both the tram wheel tires and the rails are made of steel. Due to the fact that the weight of the tram itself and the passengers is significant and a tram constantly brakes, accelerates and makes turns, there is a lot of wear on both the thread and the flange. The profile of a new wheel tire is shown in the picture on the left in Figure 6. The shape of the wheel tire changes due to wear, which results in a wheel tire like the one shown in the picture on the right in Figure 6. The flange of a worn wheel is much lower and thinner and the thread has a hollow shape. The shape of the wheel profile is very important for safe and comfortable transport. In order to keep the wheels of a tram in proper shape for operation, the wheels have to be serviced from time to time and replaced when necessary.
When the profile of a wheel tire changes too much, it is not safe anymore to use it. The shape of the profile wheel tire can be restored by turning the wheel to a smaller diameter. A schematic example of this process is shown in Figure 7. This maintenance action is performed on a wheel lathe mounted in a
Figure 4: Tram wheels on the rail Figure 5: Drawing of a Citadis wheel, with cross section.
7 pit underneath the tracks at the Beverwaard depot, shown in Figure 8. The tram is positioned on the lathe, so the wheels do not have to be removed from the bogie.
A wheel can only be turned a number of times until the wheel tire is too thin. When the wheel profile is worn out and the wheel tire cannot be turned anymore, the tire needs to be replaced by a new tire. The removal of the tire from the wheel core takes a long time and therefore this is not done while the wheels are on the tram. The entire wheels are replaced by wheels with new tires, to minimize the time a tram needs to be at the maintenance department. This maintenance action is performed in the wheel replacement pit, also at Beverwaard, shown in Figure 9. At this unit the wheels can be replaced, using lifting aids, see Figure 10.
In 2010 the total costs for the maintenance of the trams was €6,900,000 and the costs for the maintenance of the tram wheel tires alone was €900,000, which is 13 % of the total. This indicates that the maintenance of the wheels is a large expense and a lot of money can be saved there. The maintenance of the wheels is also a very time consuming process. Of the total 54,000 man hours a year, the maintenance of the wheels takes 5,700 man hours, which is 11 %.
Figure 7: Worn profile (1) and restored profile (2) Figure 8: Wheel lathe depot at Beverwaard
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2.5
Measuring systems
In order to get a better insight in the state of the tram wheels the RET bought measuring systems which are installed at both depot at Beverwaard and the depot at Kralingen, shown in Figure 11 and Figure 12 respectively. The specifications come from the manual supplied by the manufacturer of the measuring systems, Talgo (Patentes Talgo S.L., 2011).
When the wheels of a tram need to be measured, the tram can drive across the measuring system with a maximum velocity of 10 km/h. In Figure 13 a schematic example is shown of the working principle of the measuring system.
In the figure the wheel, the lasers and the camera unit indicated by 1, 2 and 3 are shown. When a wheel passes the lasers, the two diffused beams together form a vertical plane. When the center of the wheel passes this plane, the cameras are activated, one from the side and one underneath the wheel. Both cameras take a picture of the surface of the wheel profile, highlighted by the lasers, as shown in Figure 13 as the red part of the wheel profile indicated by 1 in Figure 13. The system then uses both pictures combined to create an accurate image of the profile. Because the laser light is very bright, the outline of the profile is clearly visible in the picture. An example of such a picture is shown in Figure 14. From these pictures a number of wheel parameters are measured, which are listed below and defined as shown in Figure 15:
Figure 11: Wheel measuring system at Beverwaard Figure 12: Wheel measuring system at Kralingen
9 - Flange height hb
- Flange thickness eb - Flange steepness qR - Diameter of the wheel D - Wheel gauge Ei
Another wheel parameter is the hollow wear P, which is not indicated in the figure. The hollow wear P is a measure of the curve of the wheel thread. The wheel thread should be straight, but it get a hollow curve caused by very local wear. The theoretical measuring accuracy of all the measured wheel parameters can be found in Appendix B along with a more detailed description of the working principle of the measuring system.
2.6
Maintenance criteria
Currently the measurement data is manually analyzed and compared to specific criteria, the so called rejection limits, to determine if a wheel is still suitable to use. The criteria can be divided in three groups:
- Wheel parameters separately - Wheel gauge
- Diameter differences
The flange thickness eb, flange height hb, diameter D and the hollow wear P of a tram wheel have a lower limit only, because these wheel parameters always become smaller due to wear. This means the value of a particular parameter is not allowed to be smaller than this limit. The flange steepness factor qR and the wheel gauge Ei have both an upper and lower limit, which are both not allowed to be crossed. If the flange becomes too steep, it affects the convenience but if the flange is not steep enough the possibility of derailment becomes higher. There are also restrictions for the differences between the diameters of certain wheels. In order to provide a good overview of the different relations for the allowed differences in wheel diameters, this is visualized in Figure 16.
10 b c b a d a a
Figure 16: Relations between allowed differences in wheel diameter
The limit for the diameter difference of the wheels of the same axle is indicated by a. There is also a limit on the difference in diameter between the wheels on one side of a bogie indicated by b, because these wheels are connected to the same motor. Because the second bogie is not powered, this bogie has a different limit of the diameter difference on one side of the bogie, indicated by c. Finally there is also a limit on the average diameter difference between the first and the third bogie, because these bogies are both powered and if the difference in diameter becomes too large, the engines will not be loaded equally and slip will occur.
For all rejection criteria values have been chosen based on regulations and experience. To indicate when a tram wheel almost needs maintenance, warning limits have been introduced for every criterion. The entire list with all criteria, with the currently used upper and lower limits is shown in Table 1.
Table 1: Current wheel profile warning and rejection criteria
Variable name Warning (-) Rejection (-) Warning (+) Rejection (+)
Flange thickness 10.8 mm 10.3 mm X X
Flange height 14.5 mm 14.0 mm X X
Wheel diameter 532.0 mm 530.0 mm X X
Allowed hollow wear X X 0.8 mm 1.0 mm
Flange steepness factor 0.7 mm 0.5 mm 3.2 mm 3.5 mm
Wheel gauge 1384 mm 1381 mm 1386.0 mm 1386.5 mm
Wheel diameter differences: a) b) c) d) -2.0 mm -2.0 mm -3.5 mm -38.0 mm -3.0 mm -3.0 mm -4.0 mm -40.0 mm 2.0 mm 2.0 mm 3.5 mm 38.0 mm 3.0 mm 3.0 mm 4.0 mm 40.0 mm
2.7
Current analysis process
The measurement systems automatically generate spreadsheet files with the measurement data of the wheels of all the trams, which are stored on a server. Once every month these files are loaded manually into a MATLAB program, which is used to filter the obvious measurement errors, sort the data and
11 visualize the results using graphs. These results have to be analyzed manually for all 113 trams to create a list of the trams with the highest maintenance priorities. Because each tram has 12 wheels and for each wheel several measured wheel parameters need to be analyzed, this analysis process is very time consuming. The priority list that is created is used by the maintenance department to make their planning for the turning and replacing of the wheel tires. The entire process is shown schematically in Figure 17.
Manual Automated
Measure
Filter, sort and visualize data in
MATLAB
Measurement
Data Analyze
results
Graphs and tables
per tram Maintenance at
BVW
Priority list
Store data
Measurement Data
Figure 17: Process steps of the current analysis of the measured data.
In Figure 18 and Figure 19 examples are shown of the graphs that are currently used for the analysis of the data.
Figure 18: Example of wheel diameter graphs
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These graphs are already filtered in MATLAB, but they are still full of noise and errors, which makes the interpretation very difficult and subjective.
2.8
Problem definition
As described in the introduction, the current maintenance planning based on the measurement data is far from optimal. The theoretical measuring accuracy is very high, but there are more disturbances in the measurement data than was initially counted on. Therefore the analysis software provided by the manufacturer of the measuring system is not used and the entire analysis process is performed manually. The fact that there are so many disturbances makes the interpretation of the measurement data much more difficult and time consuming. Therefore only a few of the measured wheel parameters are analyzed and this analysis is only performed once a month. Only the criteria for the wheel diameter D1and D2, flange thickness eb1and eb2, wheel gauge Ei and wheel diameter differences b and c are checked.
Due to the fact that the analysis of the measurement data is only performed partially and monthly, there are many trams in operation which do not comply with the entire set of criteria listed in Table 1 and there are large differences between the states the wheels of different trams in the fleet. In the optimal situation all trams comply with all the specified criteria and maintenance actions are performed on the wheels of trams which almost reach a rejection limit.
In order to minimize the number of trams that are not allowed to be used, because one of the rejection limits has been reached for a wheel on these trams, the measurement data must be used more intensively to optimize the maintenance planning. First the measurement data must be analyzed and the quality of the filtered data needs to be improved. Then the current analysis process needs to be automated in order to save time and make it possible to analyze all measured wheel parameters and perform this analysis more frequently. Then the analysis process needs to be extended with a prediction of the wear, to provide an optimal planning directly to the maintenance department. For the RET a better data analysis can enable them to have a more clear insight in the overall state of the tram wheels.
2.9
Scope
The scope of this thesis is the entire process that translates the measurement data into a planning for the tram wheel maintenance. There are a lot of improvements possible in this process to get a better view of the state of the wheels of different trams, which can help optimize the maintenance planning. The main goal in this thesis is to provide an optimal planning of the maintenance of the tram wheel tires to the maintenance department, based on the wheel measurement data. The scope is defined as the red block, shown in Figure 20.
13 SCOPE
Automated
Measure visualize data in Filter, sort and MATLAB
Measurement
Data Analyze
results
Graphs and tables
per tram Maintenance at
BVW Optimal Planning Store data Measurement Data
Figure 20: Process steps of the current analysis of the measured data, with indicated scope of this thesis.
The current analysis process will be automated, optimized and also extended to immediately provide an optimal maintenance planning to the maintenance department.
2.10
Research questions
The main goal in this thesis is to provide an optimal planning for the maintenance of the tram wheel tires, based on the wheel measurement data. This has led to the main research question:
“How can the current tram wheel maintenance planning be optimized based on measurements of the tires of the tram wheels?”
Because the optimization of the maintenance planning can be realized by a number of different improvements in the current analysis process, five sub-questions have been composed:
1. How can the currently available data from the wheel tire measuring systems be analyzed in order to enable automated decision making and wear predictions?
2. How can the measurement data be analyzed automatically to determine of which trams the wheel tires need maintenance?
3. How can the analyzed measurement data be used to make predictions for the wear of the tram wheels to help optimize the planning?
4. How can the measurement data provide information on the type of maintenance activity that is needed and the expected time this will take?
5. How can all the obtained information from the measurement data be used to automatically generate an optimal maintenance planning for the tram wheels?
2.11
Approach
The entire process that translates the measurement data to a maintenance planning will be investigated in this thesis. This process can be considered a black box with as input the measurement data and as desired output an optimal maintenance planning, as shown in Figure 21.
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Automated analysis program
Automated Measure Measurement Data Maintenance at BVW Optimal Planning Store data Measurement Data
Figure 21: Scope of this thesis.
There are a number of steps that have to be taken to achieve the desired optimal planning for the maintenance of the wheel tires. These steps have been described by the five sub-questions in paragraph 2.10. These five sub-questions are illustrated as blocks I, II, III, IV and V in Figure 22 and together they form the entire process of translating the measurement data to an optimal planning.
Automated Measure
Measurement
data Maintenance at
BVW
Tram which require maintenance Store data Measurement data SAP (database) Mileage information I II
Correct and coupled
data III V PlanningOptimal
IV
Correct and coupled data Prediction of wear Extra planning information Prediction of wear
Figure 22: Build-up of this thesis
To answer the first sub-question a data analysis will be performed in block I. The data from the measuring systems will be coupled with the available information on the mileage of the different trams. Then the coupled data will be analyzed in order to filter all disturbances from the dataset and the data will be smoothed to make reliable maintenance decisions. This block will be discussed in chapter 3. In chapter 4 blocks II and III will be discussed. In this chapter methods will be proposed and implemented to determine which trams directly need maintenance and to predict when the other trams will need maintenance. Then in chapter 5 block IV and block V will be investigated. In this chapter the wheel tire maintenance process and the characteristics of the tram wheels are analyzed to develop a method to automatically determine the required maintenance and to calculate an estimated time needed for this maintenance. Finally the possibility to automate and optimize the entire maintenance planning entirely based on all the available information.
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3 Data analysis
In this chapter the first research question is answered:
“How can the currently available data from the wheel tire measuring systems be analyzed in order to enable automated decision making and wear predictions?”
First in paragraph 3.1 the problems with the quality of the currently available data generated by the measuring systems will be explained. Then in paragraph 3.2 methodologies will be proposed to solve these problems based on the available literature and own research. In paragraph 3.3 the implementation of these methodologies for the application of the wheel tire measuring systems will be described and tested. Finally in paragraph 3.4 the conclusions of this chapter will be discussed and the sub-questions are answered.
3.1
Problem
There are a number of reasons that make the currently available measurement data very hard to use. First of all there are small disturbances, which will be referred to as noise. This noise can be caused by a number of reasons. First of all the measuring systems only measures each wheel on one point of the circumference, which can cause small deviations due to the fact that wheels are not necessarily perfectly uniform. There are also theoretical measuring accuracies for each measuring system, which can be found in Appendix B, which also cause noise in the datasets. An example of actual measurement data of the wheel diameter of a tram is shown in Figure 23. The graph on the right is the same as the one on the left only zoomed in to indicate the effect of the noise in the dataset.
From observation of these graphs, it can be established that there are also large peaks in the datasets, which are obviously not correct measurements. These peaks are hard to prevent, because they can be caused by many different reasons. First of all a measurement error is caused by the measuring system itself. It can also be caused by various external disturbances. The measuring system measures optically, which means the measured value for a wheel parameter can be influenced by dirt or water on the
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surface of the wheel profile. These peaks can be caused by a number of reasons, such as errors in the measuring systems’ software, dirt or water on the surface of the wheels or a number of other reasons. The causes for the noise and peaks are not investigated, because the measuring systems are beyond the scope of this thesis.
The third problem with the datasets is the fact that maintenance actions, such as turning and replacing wheel tires, result in abrupt changes in the wheel parameters. The magnitude of these abrupt changes can be very small for some wheel parameters and of very significant size for other wheel parameters. The presence of these abrupt changes, which can be considered as discrete steps in an otherwise continuous dataset, makes it difficult to eliminate the peaks and small noise from the dataset. In Figure 24 an example of a datasets is shown in which discrete steps caused by a maintenance actions are shown, indicated by the black arrows. The graph on the right is an enlarged version of the graph on the left.
In the optimal situation the exact point in time would be known for each discrete step caused by a maintenance action, to make it possible to split the large dataset into sections between two maintenance actions. To achieve this, the information from the maintenance department could be used. Currently the information on the maintenance actions is written down manually by the workers at the maintenance department. The manually recorded data is often incomplete and sometimes incorrect due to human errors. This makes it impossible to use, when a high reliability is required. It is also possible to get the information from the software of the wheel lathe system directly. Creating a coupling between that system and the server on which the wheel measuring systems store their data is very complicated and expensive. This will also only provide information on the turning of the wheels and nothing on the replacing of the wheels. This would also only be a solution for the future data that comes from the measuring systems, while the historic data is very important for further analysis in chapter 4. This means that while it is very complicated and expensive, it still is only a partial solution to the problem.
Therefore it is more reliable and less expensive to determine when the discrete steps caused by one of the maintenance actions have occurred directly from the measurement data. This eliminates the possibility of human errors.
17 The first step in improving the quality the analysis of the measurement data is to link the measurement to the information on the mileage of the different trams. This provides a much more realistic view of the wear rate, then in the current situation, where the values of the different parameters are only linked to time. This coupling of the two databases will be explained in paragraph 3.3, along with the implementation of the proposed methodologies to solve the other problems with the measurement data.
The desired output from this chapter:
- Dataset linked to the travelled distance of a tram.
- Dataset divided into sections between to maintenance actions.
- Each section separately filtered and smoothed to remove all noise and peaks.
3.2
Methodologies
In this paragraph methodologies will be proposed to achieve the desired output described in the previous paragraph. This requires methods to recognize discrete steps in time series and methods to filter and smooth the datasets once they are separated. First the existing methods found in literature will be discussed and then a methodology is proposed.
3.2.1 Existing methods
Over the years a lot of researched has been done on the subject of pattern recognition of change detection. In this paragraph a number of the found methods will be discussed briefly in order to find the best method for this particular application.
Most methods of pattern recognition have as primary goal the classification of a dataset or different parts of a dataset. This is used in many applications such as web searching, retrieval of multimedia data, face recognition and also data mining (Jain, Duin, & Mao, 2000). The application of the data from the wheel measuring systems can be considered data mining, which could mean it is suitable for one of the existing pattern recognition methods. However, most methods for pattern recognition are designed to deal with very complex signals of very large datasets, which is not the case for this application. In
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the application of the data of the wheel measuring system there is no need for the recognition of the pattern, but only abrupt changes in the dataset need to be detected. In 1993 Bassevile et al. (Basseville & Nikiforov, 1993) describe a number of methods in their book to detect abrupt changes in a dataset. In this book a number of methods such as Shewhart control charts (Shewhart, 1931), finite or infinite moving average control charts, filtered derivative algorithms and the cumulative sum algorithm (CUSUM) (Page, 1954) are covered. These methods all use a similar principle and are designed to detect a change in the mean of a normally distributed sequence with constant variance. A dataset is evaluated as a distribution and the mean value and variance are determined. Then when a change in value in the dataset occurs, the probability density is calculated to determine whether it is within a certain probability that this value comes from the same distribution or if it is from a distribution with a different mean value. This is partially the same as is required in the application of the data of the tram wheel measuring systems. In this case there is also no difference between the variance before and after the step, but an abrupt change cannot be considered a change in the mean value, because the datasets change over time. The value of the wheel parameters gradually change and is not normally distributed around a constant mean value. When the length of a dataset increases and the value of the wheel parameter decreases in time, the mean of the distribution will change gradually and the variance of this distribution will increase. This makes it harder to detect very small abrupt changes in the dataset. Another problem is the great number of peaks in the datasets, along with the sometimes small step sizes caused by maintenance actions. Another example of actual measurement data of the wheel diameter of a tram is shown in Figure 26. The graph on the right is the same as the one on the left only zoomed in to indicate the effect of the noise in the dataset.
If these peaks in the dataset are in both positive and negative direction they will increase the variance in the distribution drastically, which also makes it very unlikely that small abrupt changes in the datasets will be detected. If the majority of these peaks have the same direction, as in the example in Figure 26, it offsets the mean of the distribution, resulting in possible false detections. In these graphs it is very difficult to see where the maintenance actions have occurred. Especially in the graph on the right it is very clear that the number of peaks in the dataset makes it rather impossible to use statistic methods
