POLITECNICO DI MILANO
School of Industrial and Information Engineering
Master of Science in Mechanical Engineering
AN INNOVATIVE MAINTENANCE METHODOLOGY FOR
FREIGHT WAGONS COMBINING PROPORTIONAL
HAZARD MODEL AND LIFE CYCLE COST
Supervisor: Prof. Luca FUMAGALLI
Co-Supervisors: Eng. Irene RODA
Eng. Adalberto POLENGHI
Master Thesis of:
Riccardo DENARI 858604
Marco DEROSSI 859087
SUMMARY
ABSTRACT (ENGLISH VERSION) ... I ABSTRACT (ITALIAN VERSION) ... II EXECUTIVE SUMMARY (ENGLISH VERSION) ... III
FREIGHT TRANSPORT SYSTEM IN EUROPE ... III MAIN CRITICALITIES ON FREIGHT RAIL TRANSPORTATION ... IV APPLICATION FIELDS OF THE PROPORTIONAL HAZARD MODEL ... V MAINTENANCE METHODOLOGY FOR FREIGHT WAGONS ... V
INPUT MAINTENANCE DATA ... VII PROPORTIONAL HAZARD MODEL (PHM) ... VIII LIFE CYCLE COST (LCC) ANALYSIS ... IX WIZARD TOOL... IX MAINTENANCE POLICY OPTIMIZATION ... X APPLICATION CASE STUDY ... XI WORK LIMITATIONS AND FUTURE DEVELOPMENTS ... XII
EXECUTIVE SUMMARY (ITALIAN VERSION) ... XIII
METODI DI TRASPORTO MERCE IN EUROPA ... XIII PRINCIPALI CRITICITÀ DEL TRASPORTO MERCI SU ROTAIA... XIV CAMPI DI APPLICAZIONE DEL PROPORTIONAL HAZARD MODEL ... XV METODOLOGIA DI MANUTENZIONE PER I TRENI MERCI ... XV
DATI IN INPUT ... XVII PROPORTIONAL HAZARD MODEL... XVIII ANALISI DEL LIFE CYCLE COST (LCC) ... XIX WIZARD TOOL... XIX POLITICA MANUTENTIVA OTTIMIZZATA ... XX CASO APPLICATIVO ... XXI LIMITAZIONI DEL LAVORO E FUTURI MIGLIORAMENTI ... XXII
1 RESEARCH OBJECTIVES AND PROBLEM STATEMENT ... 1
1.1 RESEARCH OBJECTIVES ... 1
1.2 PROBLEM STATEMENT ... 3
1.3 BACKGROUND ON FREIGHT WAGONS MAINTENANCE MANAGEMENT ... 5
1.3.1 CORRECTIVE MAINTENANCE ... 5
1.3.2 TIME-BASED MAINTENANCE ... 5
1.3.3 CONDITION-BASED MAINTENANCE ... 6
1.3.4 TIME-BASED VS CONDITION-BASED ... 8
1.3.5 PREDICTIVE MAINTENANCE ... 11
1.4 OBJECT OF THE ANALYSIS: THE BOGIE ... 12
2 LITERATURE REVIEWS ...18
2.1 LITERATURE REVIEW ON FREIGHT TRAIN CRITICALITIES ... 18
2.1.1 SCHEDULING ISSUES ... 23
2.1.2 RAILWAYS INFRASTRUCTURE MAINTENANCE ... 25
2.1.4 FREIGHT TRAIN MAINTENANCE ... 25
2.2 LITERATURE REVIEW ON PROPORTIONAL HAZARD MODEL ... 28
2.2.1 ARTICLE 1: PHM APPLICATION TO BRAKE DISCS ON HIGH-SPEED TRAINS ... 32
2.2.2 ARTICLE 2: PHM APPLICATION ON RAIL WAGON BEARINGS ... 32
2.2.3 ARTICLE 3: PHM APPLICATION IN RAILWAY WHEEL FAULT DETECTION ... 32
2.2.4 ARTICLE 4: PHM APPLICATION ON THE MEAN RESIDUAL LIFE OF RAIL WAGON BEARINGS... 33
2.2.5 ARTICLE 5: PHM APPLICATION ON RAILWAY VEHICLE GEARBOX ... 33
2.2.6 ARTICLE 6: PHM APPLICATION ON THE RESIDUAL USEFUL LIFE FOR TRAIN’S ROLLING BEARING ... 34
2.2.7 ARTICLE 7: PHM APPLICATION ON RAILWAY DOOR SYSTEMS ... 34
2.2.8 RECAP OF THE MAIN PHM’S ARTICLES IN RAILWAY FIELD ... 37
3 MAINTENANCE METHODOLOGY FOR FREIGHT WAGONS ...38
3.1 METHODOLOGY OVERVIEW ... 41
3.2 INPUT MAINTENANCE DATA ... 42
3.3 PROPORTIONAL HAZARD MODEL ... 45
3.3.1 INTRODUCTION ... 45
3.3.2 PHM BASICS ... 45
3.3.3 DIFFERENT FUNCTIONAL FORMS OF ψ(Z,γ) ... 46
3.3.4 COVARIATES ... 47
3.3.5 ESTIMATION OF THE REGRESSORS... 51
3.3.6 WEIBULL BASELINE HAZARD FORM ... 53
3.3.7 ADVANTAGES OF THE PHM ... 53
3.3.8 APPLICATION OF THE PHM ON THE Y25 BOGIE ... 54
3.4 LIFE CYCLE COST (LCC) ANALYSIS ... 56
3.4.1 LCC BASICS ... 56
3.4.2 APPLICATION OF THE LCC MODEL TO Y25 FREIGHT WAGON BOGIE ... 58
3.4.3 LCC OF THE WHEELSET (TRADITIONAL APPROACH) ... 60
3.4.4 ACTUALISATION PROCESS ... 66
3.4.5 RESULTS OF THE LCC CALCULATION... 68
3.4.6 EFFECT OF THE PHM IN THE LCC CALCULATION ... 71
3.5 WIZARD TOOL ... 73
3.5.1 INPUT ... 74
3.5.2 OUTPUT ... 74
3.6 MAINTENANCE POLICY OPTIMIZATION ... 76
4 APPLICATION CASE STUDY ...78
4.1 INTRODUCTION ... 78
4.2 TIME BETWEEN FAILURES GENERATION ... 78
4.3 WHEEL FLATNESS ... 79 4.4 DATASET 1 ... 79 4.4.1 LCC WITH PHM ... 83 4.5 DATASET 2 ... 85 4.5.1 LCC WITH PHM ... 89 4.6 DATASET 3 ... 91 4.6.1 LCC WITH PHM ... 95 4.7 DATASET 4 ... 96 4.7.1 LCC WITH PHM ... 100 4.8 DATASET 5 ... 102 4.8.1 LCC WITH PHM ... 107 4.9 RESULTS ... 107
5 CONCLUSIONS ... 109
5.1 FREIGHT TRAIN CRITICALITIES ... 109
5.2 APPLICATION FIELDS OF THE PHM ... 110
5.3 PHM FOR FREIGHT WAGONS ... 110
5.4 INTEGRATION OF PHM AND LCC ... 110
5.5 WORK LIMITATIONS ... 111
5.6 FUTURE DEVELOPMENTS ... 111
APPENDIX A – MATLAB SCRIPT ... 112
APPENDIX B – GUI SCRIPT ... 115
APPENDIX C – WEIBULL PARAMETERS ESTIMATION ... 123
MAXIMUM LIKELIHOOD ESTIMATION (MLE) METHOD ... 124
METHOD OF MOMENT (MOM) ESTIMATOR ... 126
MEDIAN RANK REGRESSION (MRR) ESTIMATOR ... 127
COMPARISON OF ESTIMATION METHODS ... 129
BIAS ... 129
VARIANCE ... 130
MEAN SQUARE ERROR (MSE) ... 130
LIST OF FIGURES
Figure 1 - Freight land transportation actual subdivision ... III Figure 2 - Percentage of articles distribution within different criticalities ... IV Figure 3 - Framework of the proposed maintenance methodology ... VI Figure 4 - The Y25 bogie [4] ... VII Figure 5 - Maintenance wizard ... X Figure 6 - Covariates effect on the reliability ... XI Figura 7 - Suddivisione attuale del sistema di trasporto merci su terra ... XIII Figura 8 - Distribuzione in percentuale degli articoli secondo le diverse criticità ... XIV Figura 9 - Framework della metodologia manutentiva proposta ... XVI Figura 10 - Carrello ferroviario Y25 [4] ... XVII Figura 11 - Maintenance wizard ... XX Figura 12 – Effetto delle covariate sull’affidabilità ... XXI
Figure 13 - Percentage of total tonne-kilometers [6] ... 3
Figure 14 - Wheel & axle set [14] ... 13
Figure 15 - Comparison of effect of track irregularities between single-axle bogie and two-axle bogie [14] ... 13
Figure 16 - Non-articulated bogie & articulated bogie [14] ... 14
Figure 17 - Various axle box suspensions [14] ... 15
Figure 18 - Schematic diagram of air brake system on vehicle in application position [15] ... 16
Figure 19 - The Y25 bogie [4] ... 17
Figure 20 – Freight train criticalities ... 19
Figure 21 - Article distribution within the different categories ... 23
Figure 22 - Poland rail network [141] ... 24
Figure 23 - Cost allocation for the wheelset, the braking system and the suspension system [4] ... 27
Figure 24 - Article composition after the third screening from Scopus ... 29
Figure 25 - Article composition after the third screening from Web of Science ... 30
Figure 26 - Article composition after the fourth screening ... 31
Figure 27 - RCM maintenance decision flow chart [168] ... 36
Figure 28 – Framework of the proposed maintenance methodology ... 38
Figure 29 – Detailed flowchart of the proposed methodology ... 40
Figure 30 - Framework of the proposed maintenance methodology, focus on the input data ... 42
Figure 31 – Framework of the proposed maintenance methodology, focus on the PHM ... 45
Figure 32 - Plots of hazard function vs time... 49
Figure 33 - Effect of the covariates on the hazard rate ... 51
Figure 34 - Framework of the proposed maintenance methodology, focus on the LCC ... 56
Figure 35 - LCC model [13] ... 57
Figure 36 - RBD & Montecarlo simulation [179] ... 57
Figure 37 - Cost breakdown structure [4] ... 59
Figure 38 - Cumulated cash flow with MTBF = 5000 h ... 69
Figure 39 - Cost allocation with MTBF = 5000 h ... 70
Figure 40 - Cumulated cash flow with MTBF = 7500 h ... 70
Figure 41 - Cost allocation with MTBF = 7500 h ... 71
Figure 42 - Framework of the proposed maintenance methodology, focus on the wizard tool ... 73
Figure 43 - Input of the wizard ... 74
Figure 44 - Output of the wizard ... 75
Figure 46 - Covariates effect on reliability ... 76
Figure 47 - Reliability with and without PHM, dataset 1 ... 80
Figure 48 - Cost per unit time, dataset 1 ... 81
Figure 49 - Replacement decision graph, dataset 1 ... 82
Figure 50 - Comparison between LCC computed with the traditional approach and with the PHM, dataset 1 ... 84
Figure 51 - Reliability with and without PHM, dataset 2 ... 86
Figure 52 - Cost per unit time, dataset 2 ... 87
Figure 53 - Replacement decision graph, dataset 2 ... 88
Figure 54 - Comparison between LCC computed with the traditional approach and with the PHM, dataset 2 ... 90
Figure 55 - Reliability with and without PHM, dataset 3 ... 92
Figure 56 - Cost per unit time, dataset 3 ... 93
Figure 57 - Replacement decision graph, dataset 3 ... 94
Figure 58 - Comparison between LCC computed with the traditional approach and with the PHM, dataset 3 ... 96
Figure 59- Reliability with and without PHM, dataset 4 ... 97
Figure 60 - Cost per unit time, dataset 4 ... 98
Figure 61 - Replacement decision graph, dataset 4 ... 99
Figure 62 - Comparison between LCC computed with the traditional approach and with the PHM, dataset 4 ... 101
Figure 63 - Reliability with and without PHM, dataset 5 ... 104
Figure 64 - Cost per unit time, dataset 5 ... 105
Figure 65 - Replacement decision graph, dataset 5 ... 106
Figure 66 - Recap of train criticalities articles subdivision ... 109
Figure 67 - Bathtub curve ... 123
LIST OF TABLES
Table 1 - Datasets for the case study ... XI Table 2 - Summary of the case study result ... XII Tabella 3 - Datasets per il caso applicativo... XXI Tabella 4 - Sommario dei risultati del caso applicativo ... XXII
Table 5 - Cost comparison between truck and rail freight (cents per ton-mile) [1] ... 4
Table 6 - Comparison between TBM and CBM [10] ... 10
Table 7 - Screening procedure for the literature analysis on freight train criticalities ... 19
Table 8 - Classification of articles concerning train criticalities from 2009 to 2018 ... 22
Table 9 - Screening procedure for the literature review on Proportional Hazard Model ... 28
Table 10 - Focus of the main PHM's articles in railway field ... 37
Table 11 - Summary of the input maintenance data requirements ... 42
Table 12 - Example of input file ... 44
Table 13 - Summary of the required input data ... 44
Table 14 - Advantages of the PHM ... 54
Table 15 – Data for LCC calculation ... 61
Table 16 - Corrective maintenance cost calculation ... 62
Table 17 - Preventive maintenance cost calculation ... 63
Table 18 - Opportunity cost calculation ... 64
Table 19 - Service disruption cost calculation ... 65
Table 20 - Disposal cost calculation ... 66
Table 21 - Cost items of the LCC calculation ... 68
Table 22 - NPV of the different scenarios ... 69
Table 23 - Expected benefits of the PHM ... 72
Table 24 - Datasets for the case study ... 78
Table 25 - Covariates value, dataset 5 ... 102
Table 26 - Results of the first significance test ... 103
Table 27 - Results of the second significance test ... 103
Table 28 - Results of the third significance test ... 103
I
ABSTRACT
(ENGLISH
VERSION)
Freight wagons maintenance is a challenging issue in the railway sector. In scientific literature, there are many studies aiming to solve this problem on passenger trains, where comfort and safety are considered a priority.
In freight wagon field the current maintenance approach consists in performing preventive interventions at fixed interval of time and corrective actions when needed.
Even if a more advanced maintenance methodology would provide benefits, most of the articles regarding freight wagon issues are addressed to solve other criticalities: scheduling issues, maintenance of the railway infrastructure and wagon utilization.
This thesis aims to fulfill this gap by providing a new and innovative maintenance methodology that includes the combined use of the Proportional Hazard Model (PHM) and a cost analysis performed through the Life Cycle Cost (LCC).
The challenge of this work is to find an integration between the PHM and the LCC, which are entities born for different purposes. The PHM was developed to be applied in medicine field to assess the influence of different time-independent factors (covariates) on the probability to be affected by a disease. The LCC, instead, is a cost analysis performed generally in manufacturing field in order to assess and identify all the costs involved over the life of an asset.
A solution to combine the PHM and the LCC has been proposed through the implementation of a maintenance wizard. The wizard, developed in Matlab, takes into account the factors related to the degradation process (covariates of the PHM) and returns as output the moment in which it is economically more convenient to place a preventive intervention on the basis of a cost analysis. The application of the methodology to a case study highlights the benefits of this innovative approach in terms of money saving. Moreover, since the wizard has also the characteristic to record maintenance data, it can be used in the future as a starting point for the development of a predictive maintenance approach.
II
ABSTRACT
(ITALIAN
VERSION)
La manutenzione dei treni merci è un problema complesso del settore ferroviario. Nella letteratura scientifica ci sono molti studi che si prefiggono di risolvere questo problema nei treni passeggeri, dove il comfort e la sicurezza sono considerati una priorità.
Nel campo dei treni merci l’approccio manutentivo corrente consiste semplicemente nel fare interventi preventivi ad intervalli regolari e azioni correttive quando è necessario.
Anche se una metodologia di manutenzione avanzata potrebbe portare dei benefici, la maggior parte degli articoli scientifici riguardanti la risoluzione delle criticità dei treni merci si concentra su altre problematiche: problemi di schedulazione, manutenzione delle infrastrutture e utilizzo dei vagoni.
Questa tesi vuole colmare questa lacuna fornendo una nuova ed innovativa metodologia manutentiva che includa l’uso combinato di un modello matematico, il Proportional Hazard Model (PHM) e un’analisi dei costi sviluppata attraverso il Life Cycle Cost (LCC).
La sfida di questo lavoro è trovare un punto di contatto tra il PHM e l’LCC, che sono entità nate per fini diversi. Il PHM è stato sviluppato per essere applicato nel campo medico per valutare l’influenza di diversi fattori tempo-indipendenti (covariate) sulla probabilità di sviluppare una patologia. L’LCC, invece, è un’analisi dei costi, fatta generalmente in ambito manufatturiero, per valutare e identificare tutte le voci di costo che interessano l’intera vita di un bene.
In questa tesi viene proposta una soluzione per combinare il PHM e l’LCC attraverso lo sviluppo di un maintenance wizard. Il wizard, sviluppato in Matlab, prende in considerazione i fattori correlati al processo degenerativo (covariate del PHM) e restituisce come output il momento in cui è economicamente più conveniente schedulare un intervento preventivo, individuato attraverso un’analisi dei costi.
L’applicazione della metodologia ad un case study evidenzia i benefici di questo approccio innovativo in termini di risparmio di denaro. Inoltre, siccome il wizard ha anche la caratteristica di memorizzare i dati riguardanti gli interventi manutentivi, può essere usato in futuro come punto di partenza per lo sviluppo di un approccio manutentivo predittivo.
III
EXECUTIVE
SUMMARY
(ENGLISH
VERSION)
FREIGHT TRANSPORT SYSTEM IN EUROPE
The freight transport system is important for the European Union because it allows to move goods from the producer up to the consumer. On the land freight transport is performed by road, by rail or by inland waterways.FIGURE 1 - FREIGHT LAND TRANSPORTATION ACTUAL SUBDIVISION
Figure 1 shows how freight transportation on the land is actually divided. Inland waterways are
the less used because they face environmental constraints and therefore it is a sector with a limited growth capacity. Focusing on the comparison between rail and road transportation, [1] and [2] highlight the fact that rail transportation has the advantage to limit the number of accidents and air pollution. This is why it is important to promote rail transportation.
77%
6%
17%
Road Inland waterways Rail
IV
MAIN CRITICALITIES ON FREIGHT RAIL
TRANSPORTATION
In order to achieve a higher competitivity of train transportation against truck transportation, it is important to reduce the overall costs through the limitation of some criticalities. The main criticalities are identified with a literature analysis. From it emerged that the papers regarding freight train criticalities can be classified in:
• scheduling issues
• railways infrastructure maintenance • wagon utilization
• freight train maintenance
Since, among them, freight train maintenance is the less treated (see Figure 2), this thesis wants to increase the competitivity of the freight train by implementing an innovative maintenance methodology. This methodology combines a mathematical model called Proportional Hazard Model (PHM) with a Life Cycle Cost (LCC) analysis.
FIGURE 2 - PERCENTAGE OF ARTICLES DISTRIBUTION WITHIN DIFFERENT CRITICALITIES
44%
25% 18%
13%
scheduling issues railways infrastructure maintenance wagon utilization freight train maintenance
V
APPLICATION FIELDS OF THE
PROPORTIONAL HAZARD MODEL
A literature analysis has been conducted to have a clear view of the application field of the Proportional Hazard Model (PHM). The outcome shows that almost the totality of papers concerned the medicine field. Only a small portion regards the engineering application.
The reason for this result is that the PHM was firstly developed to be applied in medicine, to evaluate the influence of different time-independent factors (covariates) on the probability to be affected by a disease. The PHM has also been adapted to the engineering field, in particular to reliability concerns, especially in manufacturing.
The application of the PHM in the railway fields regards the most vulnerable components such as bearings, door systems, braking systems, etc.
MAINTENANCE METHODOLOGY FOR
FREIGHT WAGONS
Before addressing the proposed methodology for maintenance policy improvement, a brief description of the possible maintenance approaches for freight wagons is performed. It serves as a background to develop the methodology. Typically, wagon maintenance can be carried out through [3]: • Corrective • Time-based • By time • By mileage • Condition-based
The corrective maintenance is defined as the corrective action performed to recover from a failure and to bring back the wagon to its operational state.
Corrective maintenance is, nowadays, the less used because the failure along the line can be dangerous and also expensive. Therefore a preventive approach is preferred. The simplest preventive maintenance approach is the time-based in which the maintenance interventions are performed periodically with a frequency determined through a failure time analysis. This maintenance approach can be performed by time or by mileage. The difference is that in the first case the intervention is done regardless if a corrective action has been performed a short time
VI
before, while in the second case if a failure occurs the next preventive action scheduling restarts from that moment.
A more sophisticated maintenance approach is the condition-based. In this case, the preventive interventions are not fixed according to time, but they are scheduled taking into account the condition of the components thanks to sensors that monitor some parameters directly related with the degradation process.
In this thesis, a new and innovative maintenance methodology that combines PHM and LCC is proposed. An overview of the methodology is presented in Figure 3 in which is shown a gross explanation of the main steps.
FIGURE 3 - FRAMEWORK OF THE PROPOSED MAINTENANCE METHODOLOGY
Starting from input maintenance data, (list of TBFs, set of covariates, start and end time of the inspection intervals, the censoring index) it is possible to apply the PHM to compute the reliability that, associated with the Life Cycle Cost, represents the innovative part of this thesis. The union of the PHM and the LCC allows the creation of a maintenance wizard tool capable to suggest an optimized maintenance policy. Before describing in detail the proposed maintenance methodology, an overview of the reference asset is offered.
The bogie is the reference asset of this project. It is very important for safe railway transportation and it performs the following roles:
• Support the railcar body firmly
• Run in a stable way on both straight and curved path
• Assuring a good ride absorbing the vibrations generated by the irregularities along the track • Minimize the effect of the centrifugal forces when the train follows a curved track at high
speed
• Minimize the abrasion effect on the rail
A bogie is composed of different subsystems: the wheelsets, each composed by 2 wheels and an axle, a braking system and a suspension system.
VII
FIGURE 4 - THE Y25 BOGIE [4]
It is essentially a two-axle non-articulated bogie. Each bogie (there are 2 bogies per wagon) is composed by the following sub-assembly:
• 2 wheelset, each further composed by: • 1 axle
• 2 wheels
• 1 brake system (wheel tread type), composed by: • 1 brake rigging
• 1 isolating cock • 8 brake blocks
• 1 suspension system, composed by: • 4 axle box mountings
• 16 helical springs
INPUT MAINTENANCE DATA
In order to achieve the objectives of the methodology, some input data are required.
First of all, it is necessary to have a list of time between failures (TBFs). Since in literature there are not large databases, the TBFs were simulated considering two different possible MTBFs (5000 h and 7500 h) expressed in working hours. Since the PHM is composed by a baseline hazard function modeled as a Weibull distribution is mandatory that also the TBFs follow this kind of distribution.
VIII
Another fundamental input data is represented by the set of the considered covariates which can be seen as indicators related to the degradation process. An example of covariate, used in this thesis, is the acceleration coming from the simulation on a multibody system. The covariates have to be significative and, in order to simulate the degradation process, they must be sorted in ascending or descending order.
PROPORTIONAL HAZARD MODEL (PHM)
The Proportional Hazard Model (PHM) was introduced by Sir David Cox in 1972 and it is part of the Survival Models.
The PHM is a statistical procedure for estimating the risk of failure of a component when it is subjected to condition monitoring. The model consists of two parts: the first part is a baseline hazard function that takes into account the age of the equipment at the time of inspection, 𝜆0(𝑡), while
the second part, 𝜓(𝑍, 𝛾), takes into account the variables used to monitor the health of equipment and their associated weights [5].
𝜆(𝑡 ∣ 𝑍) = 𝜆0(𝑡)𝜓(𝑍, 𝛾)
𝑍 is a row vector consisting of the covariates and 𝛾 is a column vector consisting of the regression parameters.
In this thesis, the considered baseline hazard is the Weibull one and 𝜓(𝑍, 𝛾) is modelled through an exponential function, so the formula of the PHM becomes:
𝜆(𝑡 ∣ 𝑍) =𝛽 𝛼( 𝑡 𝛼) 𝛽−1 𝑒𝑥𝑝 (∑ 𝛾𝑗𝑧𝑗 𝑞 𝑗=1 )
𝛼, 𝛽 are the Weibull parameters, respectively the scale and the shape parameters. 𝛾 represents the value of the regressors, z are the values of the measured covariates at the instant 𝑡.
The PHM can be used in different ways, the one uses in this work is for the computation of the reliability and it assumes the following form:
𝑅(𝑡 ∣ 𝑧) = 𝑒(−(𝛼𝑡)𝛽∗𝑒∑ 𝛾∗𝑧)
The main advantage coming from the application of the PHM in the reliability calculation is that the covariates are taken into account. It means that the reliability is a function of both time and covariates and not only of the time like in the traditional approach. Therefore the resulting reliability should be more accurate.
IX
LIFE CYCLE COST (LCC) ANALYSIS
Life Cycle Cost (LCC) is nowadays seen as one of the most effective approaches when a decision among a set of alternatives has to be taken on a long-term horizon.
The LCC includes the main cost items faced in the whole lifecycle of the considered asset. In the specific case of the Y25 bogie four cost items are taken into account:
• Purchasing cost • Maintenance cost • Hidden cost • Disposal cost
The purchasing cost represents the initial cost to purchase the bogie under analysis.
The maintenance cost is divided into corrective and preventive cost. The corrective item refers to the unplanned stop of the vehicle and the subsequent repairing action and generates also effects such as the disruption of the vehicle operators service or the railway network. Preventive item instead is more controllable and is carried out according to European standards.
The hidden cost is caused by unplanned stops but it is not directly related to maintenance activities. It includes the cost incurred for a failed or late delivery of goods (opportunity cost) and for the railway network disruption (service disruption cost).
The disposal cost represents the cost to be sustained to dismiss the asset. This cost could have either a positive or negative sign. In the first case it represents a real cost due to the decommissioning phase; on the other hand, the negative sign highlights that some money could be recovered from the recycling or selling of the component.
WIZARD TOOL
By putting together the PHM and the LCC it has been possible to create a maintenance wizard. It has been developed in Matlab through a tool called Graphical User Interface (GUI). The wizard is composed of two parts: the upper containing the input data and the lower containing the output data (see Figure 5).
X
FIGURE 5 - MAINTENANCE WIZARD
In the input part, the operator has to insert the identification code of the component, the starting and ending time of the considered inspection interval, the value of the measured covariates, the actual cost per intervention in both corrective and preventive cases and finally whether the component has undergone a failure or not during that inspection interval.
The wizard gives three output: the replacement decision graph (useful to understand the degradation state of the component), the moment in which perform the next inspection calculated from an economic perspective, and the required action that suggests to the operator what he has to do (replace the component or leave it as it is).
MAINTENANCE POLICY OPTIMIZATION
The introduction of the PHM in a maintenance model has the main benefit to include the effect of the covariates. Those factors allow to obtain a more accurate estimation of the reliability and therefore the resulting maintenance policy should be optimized.
XI
The effect of the covariates can lead to two different situations: • A faster decrease of the reliability function
• A slower decrease of the reliability function
FIGURE 6 - COVARIATES EFFECT ON THE RELIABILITY
The two situations described in Figure 6 lead to a different number of preventive and corrective interventions with respect to the traditional approach (using just the Weibull distribution). In the first figure, it is expected a higher number of yearly preventive interventions and hence a reduction of the corrective ones while in the second figure the number of unnecessary preventive interventions are limited and the number of correctives remains the same. In both cases, a money saving is expected: in the first case because the corrective actions (more expensive) are reduced, in the second case because the number of unnecessary maintenance interventions is limited.
APPLICATION CASE STUDY
A case study is carried out to prove the effectiveness of the methodology. Five different datasets, all regarding the same failure mode (wheel flatness) are considered (see Table 1):
MTBF (h) Wheel Flatness (mm) DATASET 1 5000 40
DATASET 2 7500 40
DATASET 3 5000 60
DATASET 4 7500 60
DATASET 5 (field data) 5000 35 TABLE 1 - DATASETS FOR THE CASE STUDY
The application of the methodology brings to a different and optimized number of preventive and corrective interventions per unit time. This has a significant impact on the maintenance cost item in
XII
the LCC calculation. Therefore, after the computation of the LCC with the traditional approach and with the PHM, it is possible to calculate the Net Present Value (NPV) of both and then, from a comparison, it is possible to see that this new methodology is capable to produce a money saving for dataset 1, dataset 2, dataset 3 and dataset 4. In dataset 5, in which are included data coming from the field, the results are not good because the methodology never suggests a maintenance intervention even with anomalous values of covariates. As a consequence, it is not possible to provide an estimation of the reduction or the increase of the maintenance actions and therefore it is not possible the recalculate the LCC with the PHM.
DATASET 1 DATASET 2 DATASET 3 DATASET 4 DATASET 5
MTBF [h] 5000 7500 5000 7500 5000 WF [mm] 40 40 60 60 35 NPV trad. [€] 74821 53167 74821 53167 / NPV with PHM [€] 67788 50442 65428 46594 / Saving [€] 7033 2725 9393 6573 /
TABLE 2 - SUMMARY OF THE CASE STUDY RESULT
From Table 2 it is possible to conclude that the dataset 3 is the one that guarantees the higher money saving.
WORK LIMITATIONS AND FUTURE
DEVELOPMENTS
The strongest limitation of this methodology is the lack of data: both TBFs and covariates have been simulated. This issue comes from the fact that railway companies show a lack of interest in developing a database that collects the record of each failure. A well-suited maintenance policy needs a strong knowledge of the failure mechanism. For this reason, the use of simulated TBFs and covariates can lead to problems of accuracy. This limitation can be overcome using the maintenance wizard, because it is designed to keep record of the data inserted by the operator during each inspection. In this way, with time, a complete and robust database can be obtained. This can be a starting point for the future development of a predictive maintenance policy.
XIII
EXECUTIVE
SUMMARY
(ITALIAN
VERSION)
METODI DI TRASPORTO MERCE IN
EUROPA
Il trasporto merci è importante per l’Unione Europea perché permette di portare beni dal produttore al consumatore. Su terra il trasporto di merci può avvenire su strada, su rotaia o attraverso vie navigabili interne.
FIGURA 7 - SUDDIVISIONE ATTUALE DEL SISTEMA DI TRASPORTO MERCI SU TERRA
La Figura 7 mostra come attualmente è suddiviso il sistema di trasporto merci su terra. Le vie navigabili interne sono le meno utilizzate perché soggette a limiti morfologici del territorio e quindi sono un settore con una scarsa capacità di crescita. Focalizzandosi, quindi, sul trasporto su rotaia e su strada, [1] e [2] evidenziano che il primo ha il vantaggio di limitare il numero di incidenti e l’inquinamento atmosferico. Questa è la ragione per cui è importante promuovere il trasporto su rotaia.
77%
6%
17%
Strada Vie navigabili interne Rotaia Strada Vie navigabili interne Rotaia
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PRINCIPALI CRITICITÀ DEL TRASPORTO
MERCI SU ROTAIA
Per rendere più competitivo il trasporto merci su rotaia è importante ridurre il più possibile i costi limitando alcune criticità. Le maggiori criticità sono state individuate attraverso un’analisi di letteratura. Da questa è emerso che gli articoli riguardanti le criticità dei treni merci possono essere classificati in:
• problemi di schedulazione
• manutenzione delle infrastrutture ferroviarie • utilizzo dei vagoni
• manutenzione dei treni merce
Siccome, tra questi, la manutenzione dei treni merci è la meno trattata (vedi Figura 8), questa tesi mira al miglioramento della competitività dei treni merci proponendo una metodologia di manutenzione innovativa. Essa combina un modello matematico chiamato Proportional Hazard Model (PHM) con un’analisi dei costi attraverso il Life Cycle Cost (LCC).
FIGURA 8 - DISTRIBUZIONE IN PERCENTUALE DEGLI ARTICOLI SECONDO LE DIVERSE CRITICITÀ
44%
25% 18%
13%
problemi di schedulazione manutenzione delle infrastrutture ferroviarie utilizzo dei vagoni manutenzione dei treni merce
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CAMPI DI APPLICAZIONE DEL
PROPORTIONAL HAZARD MODEL
Attraverso un’analisi di letteratura è stato possibile avere una chiara visione dei campi di applicazione del Proportional Hazard Model (PHM). Il risultato mostra che, quasi la totalità degli articoli, riguarda l’ambito medico. Solo una piccola porzione riguarda l’ambito ingegneristico. Il motivo di questo risultato risiede nel fatto che il PHM è stato sviluppato appositamente per essere applicato in medicina con lo scopo di valutare l’influenza di fattori tempo-indipendenti (covariate) sulla probabilità di sviluppare una patologia. Il PHM è stato poi adattato al campo ingegneristico, in particolare per studi affidabilistici in ambito manufatturiero.
L’attuale applicazione del PHM in ambito ferroviario riguarda l’affidabilità dei componenti più vulnerabili come i cuscinetti, le porte dei treni, i sistemi di frenata, ecc.
METODOLOGIA DI MANUTENZIONE PER I
TRENI MERCI
Prima di introdurre la metodologia di manutenzione innovativa, è utile una breve descrizione degli approcci di manutenzione che possono essere eseguiti sui treni merci. Tipicamente la manutenzione può avvenire con le seguenti modalità [3]:
• Correttiva • Time-based
• Per tempo
• Per chilometraggio • Condition-based
La manutenzione correttiva è definita come l’azione necessaria a riportare il vagone al suo stato operativo a seguito di un guasto.
Essa è, al giorno d’oggi, la meno usata perché un guasto lungo la tratta può rivelarsi sia pericoloso che costoso. Quindi si preferisce un approccio preventivo. L’approccio preventivo più semplice è il time-based, in cui gli interventi manutentivi sono effettuati ciclicamente con una frequenza determinata attraverso un’analisi dei guasti. Questa metodologia può basarsi sul tempo o sui chilometri percorsi. La differenza sta nel fatto che, nel primo caso, gli interventi sono effettuati senza preoccuparsi del fatto che un intervento correttivo possa essere appena stato eseguito, mentre nel
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secondo caso, se si verifica un guasto, il successivo intervento preventivo viene rischedulato tenendone conto.
Un approccio più sofisticato è il condition-based. In questo caso, gli interventi preventivi non sono schedulati tenendo conto del tempo ma considerando la condizione del componente, grazie a sensori che controllano alcuni parametri direttamente correlati con il processo di usura.
In questa tesi viene proposta una nuova ed innovativa metodologia manutentiva che combina l’uso del PHM e dell’LCC. Un’overview è presentata in Figura 9, la quale include, a grandi linee, una bozza degli step principali.
FIGURA 9 - FRAMEWORK DELLA METODOLOGIA MANUTENTIVA PROPOSTA
Partendo dai dati in input (elenco dei TBFs, insieme delle covariate, istante di inizio e di fine di ogni intervallo di acquisizione, indice di censura) è possibile applicare il PHM per calcolare l’affidabilità che, associata con il Life Cycle Cost, rappresenta la parte innovativa di questa tesi. L’unione di PHM e LCC permette di ottenere un wizard tool in grado di suggerire una politica manutentiva ottimizzata. Prima di descrivere in dettaglio la metodologia proposta è utile un’overview dell’asset a cui verrà applicata.
Il carrello ferroviario è l’asset di riferimento di questo progetto. Esso è indispensabile per un trasporto ferroviario sicuro e svolge le seguenti funzioni:
• Supportare saldamente il vagone
• Fornire stabilità durante la marcia sia in rettilineo che in curva • Assorbire le vibrazioni generate dalle irregolarità del tracciato
• Minimizzare l’effetto delle forze centrifughe quando il treno percorre curve ad alta velocità • Minimizzare l’usura della rotaia
Il carrello ferroviario è composto da diversi sottosistemi: gli assili ferroviari, ognuno composto da due ruote e da un asse, un sistema di frenata e un sistema di sospensioni.
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FIGURA 10 - CARRELLO FERROVIARIO Y25 [4]
Si tratta di un carrello ferroviario a due assi non-articolato. Ogni carrello (ogni vagone ha 2 carrelli) è composto dai seguenti componenti:
• 2 assili, ognuno dei quali ulteriormente scomponibile in: • 1 asse
• 2 ruote
• 1 sistema di frenata, composto da: • 1 timoniera del freno • 1 valvola
• 8 ceppi del freno
• 1 sistema di sospensione, composto da: • 4 boccole montate
• 16 molle elicoidali
DATI IN INPUT
Per lo sviluppo della metodologia sono necessari alcuni dati in input. Prima di tutto è necessario conoscere l’elenco dei time between failures (TBFs). Siccome in letteratura non ci sono database affidabili, i TBFs sono stati simulati considerando due possibili MTBFs (5000 ore e 7500 ore) espressi in ore lavorative. Dato che il PHM è composto da una funzione di rischio di base modellata attraverso una distribuzione Weibull, è indispensabile che anche i TBFs siano distribuiti in questo modo.
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Un altro input fondamentale è rappresentato dalle covariate che possono essere viste come indicatori del processo degenerativo di un componente. Un esempio di covariata, che verrà usato in questa tesi, è l’accelerazione trovata attraverso la simulazione su un sistema multibody. Le covariate devono essere significative e, per simulare il processo di usura, devono essere ordinate in modo crescente o decrescente.
PROPORTIONAL HAZARD MODEL
Il Proportional Hazard Model (PHM) è stato introdotto da Sir David Cox nel 1972 e fa parte dei cosiddetti Modelli di Sopravvivenza.
Il PHM è una procedura statistica per stimare il rischio di guasto di un componente quando è soggetto a condition-monitoring. Il modello è formato da due parti: la prima è la funzione di rischio base che tiene conto dell’età del componente al momento dell’ispezione 𝜆0(𝑡), mentre la seconda
parte, 𝜓(𝑍, 𝛾), prende in considerazione le variabili usate per monitorare lo stato di salute del componente [5].
𝜆(𝑡 ∣ 𝑍) = 𝜆0(𝑡)𝜓(𝑍, 𝛾)
𝑍 è un vettore riga che contiene le covariate e 𝛾 è un vettore colonna che contiene i regressori. In questa tesi la funzione di rischio base considerata è la Weibull e, 𝜓(𝑍, 𝛾), è modellata attraverso una funzione esponenziale, quindi la forma del PHM diventa:
𝜆(𝑡 ∣ 𝑍) =𝛽 𝛼( 𝑡 𝛼) 𝛽−1 𝑒𝑥𝑝 (∑ 𝛾𝑗𝑧𝑗 𝑞 𝑗=1 )
𝛼, 𝛽 sono i parametri della Weibull, rispettivamente il parametro di scala e di forma. 𝛾 contiene il valore dei regressori, z invece contiene i valori delle covariate all’istante 𝑡.
Il PHM può essere utilizzato in modi diversi, in questa tesi è stato utilizzato per la stima dell’affidabilità ed assume la seguente forma:
𝑅(𝑡 ∣ 𝑧) = 𝑒(−(𝛼𝑡)𝛽∗𝑒∑ 𝛾∗𝑧)
Il vantaggio più grande che deriva dall’applicazione del PHM sul calcolo dell’affidabilità è che le covariate sono tenute in considerazione. Ciò significa che l’affidabilità è funzione sia del tempo che delle covariate. Quindi l’affidabilià calcolata in questo modo risulta essere più precisa.
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ANALISI DEL LIFE CYCLE COST (LCC)
Il Life Cycle Cost (LCC) è oggigiorno visto come uno dei modi più efficaci per prendere una decisione tra diverse alternative in un orizzonte temporale molto lungo.
L’LCC include le maggiori voci di costo che interessano l’intero ciclo di vita dell’asset preso in esame. Nel caso specifico del carrello ferroviario Y25 quattro voci di costo sono prese in considerazione:
• Costi di acquisto • Costi di manutenzione • Costi nascosti
• Costi di smaltimento
I costi di acquisto rappresentano la spesa in cui si incorre per l’acquisto del carrello ferroviario in questione.
I costi di manutenzione si dividono in correttivi e preventivi. I correttivi si riferiscono ai costi di riparazione che seguono un guasto del veicolo non previsto che causa, inoltre, altri effetti come l’interruzione della linea. I preventivi invece sono maggiormente controllati e seguono le normative europee.
I costi nascosti sono conseguenti ai guasti che, essendo eventi non pianificati, generano ulteriori costi non direttamente collegati con l’attività manutentiva come ad esempio quelli per una mancata o tardiva consegna (costi di opportunità) e quelli relativi all’interruzione della rete ferroviaria che sono conseguenza di ritardi o cancellazioni dei treni in coda (costi per interruzione del servizio). I costi di smaltimento rappresentano il flusso di denaro necessario per smaltire l’asset. Questi costi possono avere sia segno positivo che negativo. Nel primo caso rappresentano un costo reale; nel secondo caso, il segno negativo, evidenzia che una certa quantità di denaro può essere recuperata riciclando o vendendo il componente.
WIZARD TOOL
Combinando PHM e LCC è stato possibile creare un maintenance wizard. Esso è stato sviluppato in Matlab attraverso uno strumento chiamato Graphical User Interface (GUI). Il wizard si compone di due parti: una superiore che contiene i dati in input e una inferiore che restituisce gli output (vedi
XX
FIGURA 11 - MAINTENANCE WIZARD
Nella parte degli input, l’operatore deve inserire il codice identificativo del componente, l’istante di inizio e fine dell’intervallo di ispezione, il valore misurato della covariata, il costo attuale per intervento sia nel caso correttivo che in quello preventivo e infine se il componente si è guastato durante l’intervallo in questione.
Il wizard restituisce tre output: il replacement decision graph (utile per capire lo stato di salute del componente), il momento in cui effettuare la prossima ispezione calcolato in ottica economica, e la required action che suggerisce all’operatore l’azione da intraprendere (sostituire o no il componente).
POLITICA MANUTENTIVA OTTIMIZZATA
L’introduzione del PHM in un modello manutentivo ha il principale beneficio di includere l’effetto delle covariate. Questi fattori consentono di ottenere una stima più accurata dell’affidabilità e quindi la politica manutentiva che ne deriva risulta essere ottimizzata.
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L’effetto delle covariate può portare a due diverse situazioni: • Una curva di affidabilità più pendente
• Una curva di affidabilità meno pendente
FIGURA 12 – EFFETTO DELLE COVARIATE SULL’AFFIDABILITÀ
Le due situazioni descritte in Figura 12 portano ad un diverso numero di interventi correttivi e preventivi rispetto all’approccio tradizionale (utilizzando solo una distribuzione Weibull). Nella prima figura ci si aspetta un numero più alto di interventi preventivi all’anno e quindi una riduzione di quelli correttivi mentre nella seconda figura il numero di interventi preventivi non necessari viene limitato e il numero di correttivi rimane lo stesso. In entrambi i casi ci si aspetta un risparmio economico: nel primo caso perché le azioni correttive (più costose) vengono ridotte, nel secondo caso perché il numero di interventi manutentivi non necessari è limitato.
CASO APPLICATIVO
Per dimostrare l’efficacia della metodologia proposta può essere utile introdurre un caso applicativo. Vengono considerati cinque diversi datasets, tutti riguardanti lo stesso failure mode (wheel flatness) (vedi Tabella 3).
MTBF (h) Wheel Flatness (mm) DATASET 1 5000 40
DATASET 2 7500 40
DATASET 3 5000 60
DATASET 4 7500 60
DATASET 5 (dati da campo) 5000 35 TABELLA 3 - DATASETS PER IL CASO APPLICATIVO
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L’applicazione della metodologia porta ad un numero diverso e ottimizzato di interventi correttivi e preventivi nell’unità di tempo. Ciò ha un notevole impatto sulla voce di costo della manutenzione nel calcolo dell’LCC. Perciò, dopo aver calcolato l’LCC sia con l’approccio manutentivo tradizionale sia con il PHM, è possibile risalire al Net Present Value (NPV) di entrambi i casi e poi, con un confronto, si può verificare se questa nuova metodologia porta dei vantaggi economici o meno. I risultati evidenziano che per i dataset 1, 2, 3, 4 l’applicazione della metodologia porta ad un risparmio. Nel dataset 5, nel quale vengono considerati dati da campo, non è possibile ottenere risultati soddisfacenti perché la metodologia non suggerisce nessun intervento manutentivo anche con valori di covariate anomali. Di conseguenza, non è possibile fornire una stima di un’eventuale aumento o riduzione del numero di interventi manutentivi e quindi non è possibile ricalcolare l’LCC con il PHM.
DATASET 1 DATASET 2 DATASET 3 DATASET 4 DATASET 5
MTBF [h] 5000 7500 5000 7500 5000 WF [mm] 40 40 60 60 35 NPV trad. [€] 74821 53167 74821 53167 / NPV con PHM [€] 67788 50442 65428 46594 / Risparmio [€] 7033 2725 9393 6573 /
TABELLA 4 - SOMMARIO DEI RISULTATI DEL CASO APPLICATIVO
Dalla Tabella 4 si può concludere che il dataset 3 è quello che garantisce il maggior risparmio economico.
LIMITAZIONI DEL LAVORO E FUTURI
MIGLIORAMENTI
La limitazione più grande di questa metodologia è dovuta alla scarsità di dati: sia i TBFs che le covariate sono stati simulati. Questo problema nasce dal fatto che le compagnie ferroviarie mostrano uno scarso interesse nello sviluppare un database che tenga traccia di ogni guasto. Una politica manutentiva ben strutturata necessita di una profonda conoscenza del meccanismo di guasto. Per questa ragione, il fatto che TBFs e covariate siano simulati implica problemi di accuratezza. Questo limite può essere superato utilizzando il maintenance wizard, poiché esso è progettato per tener traccia dei dati inseriti dall’operatore durante ogni ispezione. In questo modo, con il tempo, è possibile ottenere un database completo e robusto. Questo può essere considerato, inoltre, il punto di partenza per sviluppare in futuro una politica manutentiva di carattere predittivo.
1
1
RESEARCH
OBJECTIVES
AND
PROBLEM
STATEMENT
1.1
RESEARCH OBJECTIVES
This thesis is part of a European project called INNOWAG. The INNOWAG project deals with the development of the next generation of lightweight freight wagons from the design stage up to the definition of an optimized maintenance policy.
The project involves contributors coming from all around Europe: Lucchini RS and Politecnico di Milano from Italy, Newcastle University and Perpetuum Limited from United Kingdom, Technische Universitӓt Berlin and Havellӓndische Eisenbahn AG from Germany, Inertia Technology B.V from Nederland, New Opera AISBL and Union des Industries Ferroviaires Europeennes from Belgium, Vyzkumny Ustav Zeleznicni from Czech Republic and Uzina de Vagoane Aiud SA from Romania. The ambition of this thesis is to define an optimized maintenance policy using an innovative approach that takes into account a mathematical model called Proportional Hazard Model (PHM) and the Life Cycle Cost (LCC), in order to respond to major challenges in rail freight competitiveness, in relation to the increase of transport capacity and logistic capability.
The overarching goal of improving the maintenance policy for freight wagons have been decomposed in smaller research tasks, in order to go in the details of each step. In particular, the following research questions have been answered:
• What are the main criticalities in the freight railway sector that must be addressed? • What are the application fields of the PHM?
• How it is possible to tailor PHM for freight wagons?
• How to integrate the PHM in a wider methodology including the LCC analysis to assess the
cost-effectiveness of the proposed maintenance policy?
The first two questions are answered in chapter 2 where two different literature analyzes are conducted. The outcome of the first research shows four main criticalities: scheduling issues, railways infrastructure maintenance, wagon utilization and freight train maintenance. While the first three categories are largely investigated the other one highlights a gap that must be fulfilled. This result justifies the need for the development of a more accurate maintenance policy. The outcome of the second literature analysis, instead, displays in which sectors the PHM is applied with a particular focus on some examples in railway field.
2
The third question is investigated in section 3 of chapter 3, where the PHM is used for the reliability calculation. With respect to the traditional approach, in which the reliability is modelled using just the Weibull distribution, the PHM has the main advantage to consider not only the time between failures (TBFs) but also the effect of factors related to the degradation process in the reliability calculation.
The last question is discussed in section 6 of chapter 3 and in chapter 4. The effects of the PHM and those of the LCC are put together through a maintenance wizard that, using a function called cost per unit time (CPUT) is able to find the moment in which, from an economic perspective, it is more convenient to place a preventive intervention. On a long-term horizon, the number of corrective and preventive interventions will change with respect to the actual maintenance policy, allowing to understand whether or not the new approach is cost-effective.
3
1.2
PROBLEM STATEMENT
The freight transport system is fundamental for the prosperity of the European Union and its citizens: it enables the movement of goods within the single European market, bringing them from the producer to the consumer.
The freight transport system is important for the unification of the European market. This allows to stimulate the competitivity of companies, which have the benefits of operating within the largest market of the industrialized world with the same simplicity that they would have in operating in the market of their own country.
Freight transport can be performed by air, by sea or by land. In particular, land transport is divided into three modes:
• road • railway
• inland waterways
Currently, in Europe, the road transport has the higher share: in 2016 it covered the 77% of the total inland transport, while the train is used in the 17% of the cases, and inland waterways just for the 6% [6], as depicted in Figure 13.
FIGURE 13 - PERCENTAGE OF TOTAL TONNE-KILOMETERS [6]
Since transportation through inland waterways is subjected to strong environmental constraints, it is a sector with limited growth capacity [6]. Therefore, a focus on road and rail transportation is carried out.
77% 6%
17%
4
The largest part of goods moves on trucks, mainly because they offer the advantage of flexibility being able to arrive where the other transportation system can’t, but they have some drawbacks, for instance:
• air pollution • safety
• road congestion
• higher number of accidents
It is interesting to study the differences among different transportation systems, especially the two that are more impactful on the normal life of people: Table 5 provides a comparison between train and truck in term of safety and pollution. Those aspects are quantified in terms of cost per ton-mile. The meaning of ton-mile is load carried multiplied by the distance involved. The costs referred to trains were calculated in [1] while those of the trucks comes from another article [2].
ACCIDENTS AIR POLLUTION GREENHOUSE GASES
FREIGHT TRUCK 0.59 0.08 0.15
FREIGHT TRAIN 0.17 0.01 0.02
TABLE 5 - COST COMPARISON BETWEEN TRUCK AND RAIL FREIGHT (CENTS PER TON-MILE) [1]
The accidents costs are the multiplication of the number of fatal, personal injuries and property damage by an appropriate per event cost divided by the number of ton-miles.
The estimation of air pollution and greenhouse gases is more challenging because it depends on the particular type of truck/train and on the operating conditions: these values could be considered as a rough mean, keeping in mind that they are affected by a certain variability.
However, from Table 5, it can be observed that trains offer a higher level of safety and lower pollution. Thus, fostering the freight transportation may bring to higher advantages if seen from a sustainability aspect: freight transportation has lower air pollution and greenhouse gases production if compared with freight truck; moreover, freight trains have been subjected or have caused a lower number of accidents.
Promoting rail transportation may bring to higher advantages to the carriage of goods since it starts from a favorable condition with respect to road solution.
5
1.3
BACKGROUND ON FREIGHT
WAGONS MAINTENANCE MANAGEMENT
Currently, maintenance of freight wagons is performed through a series of inspections allowing for the detection of potential safety hazards (e.g. derailments) and functional issues that can affect the performances. Typically, wagon maintenance follows a schedule driven by one of several triggers [3]: • corrective • time-based • by time • by mileage • condition-basedSince nowadays the condition-based is largely adopted, after a brief description of the corrective and time-based maintenance, it will be analyzed more deeply. Then an evolution of the condition-based, the predictive maintenance will be introduced.
1.3.1 CORRECTIVE MAINTENANCE
The corrective maintenance is defined as the remedial action carried out to recover from a failure and to bring back the asset to its operational state [7].
It is the simplest and the oldest maintenance policy appeared in every industrial field and was particularly used in the past when the role of maintenance was not seen as a key to achieve better performances [8].
In the railway field the failure of a train can be very dangerous and expensive: first of all failures of critical parts (e.g. brakes) can lead to safety hazards and secondly a failure along the line can bring to a series of issues such as delays or missed delivery for which the railway company would be charged by penalties [4].
For this reason, the corrective maintenance policy in the railway field is applied only on the components for which a failure doesn’t imply important consequences.
1.3.2 TIME-BASED MAINTENANCE
The time-based approach is the most traditional preventive maintenance technique. Basically, the maintenance interventions are performed periodically with a frequency determined through a
6
failure time analysis. The scope of the analysis is to find the best maintenance interval from an economic point of view, assuring a good trade-off between costs and benefits. This maintenance approach can be performed by time (also known as clock-based) or by mileage (better known as age-based). The difference is that, in the first case, the intervention is done regardless if a corrective action has been performed a short time before, while in the second case if a failure occurs the next preventive action scheduling restarts from that moment. Since the freight wagons spend most of the time unused, if a time-based policy is selected, it is preferable to use the age-based approach. The time-based approach was traditionally used in critical components such as the braking system and the wheel [3].
Later, mileage-based methods became commonplace to perform maintenance based on the actual usage of the vehicle, rather than a specified amount of time during which a vehicle may have mostly been not used. However, mileage-based maintenance on freight wagons has a huge weakness: it requires a large volume of documentation necessary to accurately control the mileage of a multitude of individual vehicles [3].
1.3.3 CONDITION-BASED MAINTENANCE
The condition-based maintenance (CBM) technique on a moving system, such as a rail vehicle, is not a new approach to maintenance, but it has been fostered in the last years thanks to the advent of new and relatively cheap sensors, usually with also computational capability onboard. The degradation and maintenance will be connected to the geographical location. In an engineering industry, the analysis and diagnosis can take place in a stationary system, using real-time data from the machinery. In the rail industry, this data must be sent to a maintenance centre. There exist a few alternatives to do this. One alternative is to look at the rail vehicle as a machine and let the analysis and diagnosis take place on board, were only refined information of the condition leaves the vehicle. Another option is to collect real-time data at the vehicle and let the analysis and diagnosis take place at a maintenance centre. This means that a large amount of data must be transmitted to a central database. This transmission could either use Global System for Mobile communications (GSM) technique or wirelessly at train stations (Bluetooth, infrared etc.).
For an easy explanation of how a CBM on railway vehicles could work, from the sensors’ measurement up to the delivery of the data to the maintenance centre, it can be helpful a division in different steps:
• sensor module • intelligent system
• human interface on rail vehicle • maintenance centre
7
Sensor Module:
the sensor module is a fundamental part of any condition-based maintenance. The sensors are used in order to collect data from the components under analysis. The installation of GPS systems on rail vehicles permits also to associate the measured data to the train position and, in case of a fault, it allows also to understand where it has taken place.
Intelligent system:
it is necessary to process all the information coming from the monitored components. This operation is generally performed by software capable of handling complex relations. In this way, it is possible to extract important information from the data such as the Residual Useful Life (RUL). This goal can be achieved taking into account not only the measured data but also, historical data, historical conditions, failure history, maintenance history, etc. [9]. In conclusion, the intelligent system has the role to:
• assess what component shows failure symptoms • help in understanding the causes of a failure • help in the evaluation of the RUL
Human interface on rail vehicles:
the use of powerful software to process the data measured from a rail vehicle can be helpful but remove humans in maintenance planning would be a mistake. This is due to the fact that a large part of the information about the conditions of a vehicle comes from the inspections and the knowledge of the maintenance operators. Moreover, all the criticalities potentially dangerous for the safety that are highlighted by the software, must be presented to the operator in order to be evaluated. Humans have always the last word about a maintenance decision.
Maintenance centre:
the maintenance centre is the place where all the vehicles data are collected. In this way combining the information from the intelligent system and from the operators, it is possible to plan the best preventive maintenance for the whole fleet of vehicles.
The main problem concerning the application of a CBM policy on a train is the selection of the components to be monitored: the train is a quite large and complex asset so it is impossible to keep every component under control. Generally, the monitored components are selected on the basis of their criticalities, spending the resources on the parts in which a failure can lead to a safety hazard or to a particular expensive reparation. Examples are:
• Wheel flat detection • Brakes
8 • Wheel bearings
• Filters
• Water and air pressure • Rotating parts
• Derailment
The most important problems in implementing a CBM on a rail vehicle are [9] : • False alarms:
it happens when a sensor highlights a false fault. This can be a problem because may bring to an inefficient maintenance plan, going against the principle of minimizing the maintenance expenditures.
• Running-in:
it is a preliminary phase performed when the maintenance manager decides to monitor a new component. Since there aren’t historical data the first measurements are needed to understand what is referred to normal operation and what is not. This operation requires time and it is important to be properly performed otherwise it is possible to incur in further problems such as false alarms.
• Maintenance planning:
moving from a traditional and static time-based approach to a more complex and dynamic CBM can lead to some problems. Maintenance managers have to put more trust in Computerized Maintenance Management Systems (CMMS) that are useful to control maintenance schedules, spare parts, work orders, etc.
1.3.4 TIME-BASED VS CONDITION-BASED
Since the most adopted maintenance policies applied on freight wagons are the time-based and the condition-based, a small sub-section providing a comparison between the two techniques should be interesting.
For any maintenance practice, data are one of the most important requirements.
Collecting data for time-based maintenance can be very challenging: many times historical data are not available or are unreliable due to errors during the recording. For instance, it is quite usual to have data referred to planned maintenance and not to a real failure and this can have a strong impact on the final results. Moreover, collecting data can be a very long procedure: a well-developed dataset can require even years.
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The data needed for condition-based maintenance are relatively easy to be collected because are always available. The problem is that expensive equipment is required and the companies are not always willing to invest in them. After the acquisition in both the cases, data must be cleaned with the goal to show useful information such as the current situation of the asset.
In the time-based case, the data are statistically investigated using the reliability theory with respect to the bathtub assumption, where the failure behavior is assumed to be predictable and time-dependent. In the condition-based case, data can be of two types:
• value type • waveform type
Value types are, for instance, measures of pressure or temperature, identified by a single value. In this case, they are quite easy to be managed.
Examples of waveform type are vibrations or acoustic data. The cleaning process of those data is a bit more challenging due to the presence of noises, which are an unwanted signal generated by another component that can cover the important information under investigation. The elimination or the minimization of the noise requires software tools and good knowledge which can be expensive.
Since both time-based and condition-based are preventive policies the goal is to avoid or at least minimize the risk of unplanned maintenance (corrective interventions). Despite to this similarity, the decision process is very different: the time-based follows an optimization approach, that means to use a mathematical model to select the best preventive interval minimizing or maximizing some criteria of interest such as cost, risk, availability, reliability, etc. The condition-based decision process instead is based on continuous monitoring of the equipment conditions [10].
Table 6 summarizes the content of [10]:
COMPARISON CRITERIA TIME-BASED MAINTENANCE CONDITION-BASED MAINTENANCE
Data required and collection Theory/principle:
Uses failure time/user-based data
Theory/principle:
Uses any parameter that
indicates equipment
conditions (e.g. vibration, sound, heat, etc.)
Practical issues:
• the recording of failure time data is not always available
• very sensitive due to incorrect recording and censoring effects
Practical issues:
• high data collection cost (sensors, etc.)
• exposed to noise effects, especially waveform-type data
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COMPARISON CRITERIA TIME-BASED MAINTENANCE CONDITION-BASED MAINTENANCE
• a set of adequate failure data (TBF) for the modeling process is difficult, time-consuming, and may be expensive to gather
Data analysis/modeling Theory/principle:
Uses the reliability theory based on bathtub curve assumption
Theory/principle:
Deterioration modeling
Practical issues:
• unrealistic assumptions • operating conditions are
assumed constant (e.g. environmental effects)
Practical issues:
• requires large data
samples
• data cleaning process is required, especially for waveform-type data, and it is a complex task
Decision process Theory/principle:
Use of the optimization approach
Theory/principle:
Use of the failure
estimation/prediction
approach and comparison with predetermined failure limits
Practical issues:
• difficult to model and interpret
• decision model is not always stable
• time-consuming in model development
• is more of a mathematical exercise most of the time rather than a practical application
Practical issues:
• short time to plan maintenance
• low reliability of long-term prediction results
• determination of failure limits may be biased
