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OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of WP4: Development of Fuzzy and Computational Intelligence based models for maintenance management.

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Presentation on theme: "OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of WP4: Development of Fuzzy and Computational Intelligence based models for maintenance management."— Presentation transcript:

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2 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of WP4: Development of Fuzzy and Computational Intelligence based models for maintenance management

3 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Goal: Develop intelligent models to represent railway infrastructures, maintenance, management and traffic processes

4 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS TASK 4.1: Development of fuzzy models for railway infrastructures and components Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT), SINTEF (NO). Output: D4.1 Report on fuzzy systems built for railway infrastructure component modeling Data preprocessing Model selection Model identification Model fine tuning Model validation Data preprocessing Model selection Model identification Model fine tuning Model validation Components of railway Infrastructure Geometric auscultations Maintenance data Components of railway Infrastructure Geometric auscultations Maintenance data Fuzzy and CI Models WP1 and WP2Data Mining Techniques

5 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS TASK 4.2: Development of fuzzy models for maintenance, management and traffic processes Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT). Output: D4.2 Report on fuzzy systems built for maintenance processes modeling Data preprocessing Dynamic models Ensembles CRISP-DM Data preprocessing Dynamic models Ensembles CRISP-DM Maintenance processes Maintenance operations (work orders) Traffic Maintenance processes Maintenance operations (work orders) Traffic FRBS SVM ANN FRBS SVM ANN WP1 and WP2Data Mining Techniques

6 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS TASK 4.3: Knowledge Extraction from Experts Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT), SINTEF (NO). Output: D4.3 Report on knowledge extraction from experts and combination with data-driven models K. Representation K. Acquisition K. Validation K. Representation K. Acquisition K. Validation Knowledge Extraction Knowledge base

7 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS TASK 4.4: Cooperation/Fusion of expert knowledge and data-driven models Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT). Output: D4.4 (Combined with Task 4.5) Expert Knowledge base Data-Driven Models Knowledge aggregation and fusion Combined Knowledge base

8 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS TASK 4.5: Development of multicriteria decision-making Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT). Output: D4.4 Report on multi-criteria decision making and multi-objective optimization Input Data Criteria Models and Knowledge Multiple objective optimization Mainte- nance Decisions

9 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Deliverable 4.1: Report on fuzzy systems built for railway infrastructure component modeling

10 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Use machine learning methods learn a functional relationship between features and targets Possible inputs in OPTIRAIL Historical geometrical condition data Infrastructure data (asset characteristics), such as sleeper type and curvature Available work order data Possible outputs in OPTIRAIL Predictions of geometrical condition data (min, max, mean, sd) Thresholded predictions  prediction of need for interventions Prediction of work orders (  D4.2) Feature 1 Feature 2 Feature 3...Target Feature 1 Target Feature 2 Instance1 Instance2 Instance3... Input Output Introduction

11 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of processing steps

12 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Alignment of geometrical inspection data

13 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Methods for data alignment Some methods take objects such as bridges and crossings into account Our approach is based on the correlation of excerpts/snippets of the curvature of the measurements Examples

14 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS The offset is by no means constant, but varies quite significantly due to the way the position of the train is determined (number of wheel rotations) Hence: The offset between the measurements is determined every, say, 1km, i.e. at discrete points, and linear interpolation is used in between Some remarks

15 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Dynamic Segmentation The idea is to use the available asset characteristics to determine homogeneous track sections.

16 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Predictive modeling of deterioration

17 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Two deterioration models for TQIs can be used: Linear deterioration model Exponential deterioration model (derived from the observation that the deterioration of track quality is proportional to the current quality) is the track quality at time t = 0 (immediately after a work order) Predictive modeling of deterioration

18 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS In the example of the Swedish Q-value, if no maintenance is performed, the quality gets worse, and the Q-value decreases Predicting future geometrical inspection data is „simply“ fitting the (exponential) model When work along the track is performed, the quality increases, with a jump, and the parameters of the (exponential) model may change The time series is cut into pieces by the work orders. One such piece is called a deterioration branch

19 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS An example from the data Q-value sigH = TQI used in OPTIRAIL (maximum of the sd of the long. levelling of left and right rail)

20 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Machine learning models

21 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Use nonlinear regression methods (FRBS, neural networks, SVR, random forest, etc.) to establish a relationship between asset characteristics and the parameters Q0 and b of the deterioration model for the TQI Sleeper type Rail type Traffic data Curvat ure...Q0b Asset1 Asset2 Asset3... InputOutput

22 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Experiments for Sweden (track 118 of the Iron Ore Line) Q0b (exp)#rules/units ANFIS /104 DENFIS /8 GFS.MEMETIC /35 Random Forest /500 SVR MLP /3 Random forest performs well Resulting models are not straightforwardly interpretable sigH is usually between 0 (perfect) and 3 (maintenance threshold) With an error around 0.6, we see that the approach is feasible, but has a high error, due to uncertainty in the data, data quality, etc. Non-constant attributes are used as input One model for Q0, one for b RMSE to assess quality of models

23 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Conclusions D4.1 A methodology for deterioration modelling was developed Goes beyond current state of the art Data has high amounts of uncertainty  models are currently not very accurate Possible solutions: Acquire more data (monthly (geometric) inspections) Get more information about the track (ballast type, drainage, subsoil type, etc.)

24 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Deliverable 4.2: Report on fuzzy systems built for maintenance processes modeling

25 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Predict future auscultation results, and predict from auscultations the work to do Historical geometrical auscultation data Infrastructure data predictive model Future geo- metrical aus- cultation data Future work orders predictive model, or existing (expert knowledge) model D4.1D4.2

26 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Two approaches for D4.2 Expert knowledge driven: We predict the auscultation, the expert tells us what has to be done. Simple version: Use the thresholds from D1.1  This only tells us that something has to be done, but not what Probably more expert knowledge needed to distinguish operations Data driven: Use historical work orders to learn the condition in which the asset was directly before the work order Problem: How to include policies, such as thresholds?

27 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Expert knowledge driven modeling of work orders: Idea: After having predicted an auscultation, apply the process currently implemented by the railway administrator to get work orders from the auscultations Detailed expert knowledge is needed for this approach (see also D4.3) We collected information from Spain (and some information from Sweden). E.g.: ParametersMaintenance action Longitudinal levelAutomatic Tamping AlignmentAutomatic Tamping Cant Automatic Tamping/ Ballast renewal GaugeFastening renewal

28 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Data driven modeling of work orders: Longitu dinal left D1 …Alignm ent D1 …Twist9 m Work to do Asset1Nothing Asset2Tamping Asset3Sleeper replacement...… Input Output

29 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Data driven modeling of work orders: Problems: Is the information in the data? We need historical work orders and geometrical data that fit together, also, we need sufficient amounts of work orders for every type of work to be done. Example: With 5 historic sleeper renewals difficult to build a model for this type of operation

30 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results Sweden: Good results especially for Random Forest: 100% recall (this means that all tamping work orders are correctly classified) 91% precision (which means that when the classifier determines that a tamping should be performed, this is correct in 91% of the cases) ErrorRecallPrecision Random Forest0.59%100.00%91.18% SVM10.30%99.56%37.25% FRBCS.W6.09%0.44%100.00% GFS.GCCL6.12%0.00%N/A

31 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Modeling of the effect of a maintenance operation, Lifecycle modelling

32 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS We need a TQI which adequately assesses deterioration behavior through time, not at a single time point. With an exponential deterioration model, we have: At time t=0: So, Q‘ as the product of Q0 and b can be used as a quality measure (the tangent to the TQI) We use the following formula to model the effect of a tamping operation (i, i+1): I.e., a tamping operation will worsen the quality by a constant c.

33 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS We consider the following asset: We lower the nominal track speed to 90km/h. The model of D4.1 predicts values of and for Q0 and b, respectively We set the threshold of triggering a work order to 3 Lifecycle modelling: Radius Class Speed Det. Branch Num. Sleeper AgeRail Age 1110 km/h018 years

34 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Lifecycle modelling: Black curve: lifecycle model with 110km/h nominal track speed, remaining life: years Red curve: lifecycle model with 90km/h nominal track speed, remaining total life: years

35 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Conclusions D4.2: A methodology for modelling maintenance decisions from current auscultations was developed This can be done data-driven and/or with expert knowledge We showed that the data-driven approach works well for the case of Sweden and tamping, and a random forest can classify tamping vs. no-tamping reliably We investigated the effect of tamping on deterioration behaviour, and applied/developed a model, using a constant change in Q0‘=Q0*b for exponential deterioration. We also did some modelling of traffic data from Norway, and from work orders of Spain (not shown in this presentation)

36 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Deliverable 4.3: Report on knowledge extraction process from experts and combination with data-driven fuzzy models

37 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS A questionnaire was developed Aims: Gather expert knowledge regarding infrastructure deterioration and maintenance operations Which variables/asset characteristics have an influence on infrastructure deterioration? How big is this influence? How does the geometrical measurement determine a maintenance decision? Which other data is necessary for a maintenance decision?

38 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results: We collected 26 answers: Problems: The answers differ considerably between the countries, and for all countries except Spain, not enough answers are available to make a within-country analysis The only distinction that is done is Spain vs the rest (we didn‘t take into account that this may underrepresent the answer from ADIF) CountryNumber of answers Spain (SP)17 (VIAS 16, ADIF 1) Poland (PO)3 Norway (NO)2 England (EN)1 Germany (GE)2 Austria (AU)1

39 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results (summarized): quality and condition of the ballast, the track load and the type of traffic, i.e. freight traffic and passenger traffic strongly influence track deterioration subsoil and drainage is also considered important for track deterioration, but in many cases, information along the whole track is not available deterioration of the track in curves is generally higher than compared to straight tracks The answer for the shortest tamping interval still considered feasible varies from 20 days to 12 months

40 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results (summarized): Decision for performing tamping is based on auscultation data Experts think that there exists an optimal interval between consecutive tamping operations in the sense that the useful track life is largest. Rail substitution can be based on the geometrical inspection data only in a limited way. Instead, other factors such as the rail condition (obtained from visual inspection) should be taken into account.

41 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results (summarized): There is no consensus whether or not rail grinding should be based on the geometrical inspection data. Additionally, visual and ultrasonic inspections should be considered. The replacement of sleepers can partially be based on the geometrical inspection data, especially on the gauge. Moreover, also the condition of the sleepers and of the fastenings, which can be determined by visual inspections, as well as the age of the components, should be taken into account. Finally, for ballast cleaning, there is no consensus among the experts which geometrical variables (besides the longitudinal level of the left and right rail) should be considered for ballast cleaning. Instead, information on the condition of the ballast and its contamination (determined by visual inspections) should be considered.

42 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Deliverable 4.4: Report on multi-criteria decision making and multi-objective optimization

43 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS

44 Main objectives track maintenance cost the availability/capacity the safety/quality of the track Other important factors Constraint or objective? Planning horizon (3,5,30 years) Granularity of planning in time (daily, monthly, trimestral) Granularity of planning in space (track sections, track length)

45 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Objective: Minimize maintenance cost Maintenance operation cost (MOC) in framework from Lulea: i sums over the track sections and j over the time intervals in the planning horizon. The variable r is a discount rate. : material cost for the maintenance operation in €/km : average time to perform the maintenance operation (MO) on the ith track section in hours/kilometre : total length of maintenance section in kilometres : average labour cost in €/hour : equipment cost for the maintenance operation in €/hour : cumulative load / time (in MGT or years) : interval for the maintenance operation of the ith track section in MGT (or years)

46 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Objective: Minimize maintenance cost The costs can be described with parameters that can later be easily changed by each railway operator. Example costs used by Lulea: One tamping machine is available for the considered track (otherwise external factors have to be taken into account) Constraints regarding time of day: Maintenance window of approx. 5h, which translates into maximal tamping distance per day Constraints regarding time of year Constraints regarding maintenance costs

47 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Objective: Availability and capacity The objective has the following aspects: capacity of the track punctuality of trains penalties if capacity and punctuality goals are not met Capacity loss is the sum of delays and non-availability of the track due to maintenance. Train delays occur due to parts of the track that cannot be used with their nominal track speed as their quality is not sufficient. Changes in speed should be minimal (as they waste resources and cause more maintenance necessity) Capacity may often be a constraint and not an objective

48 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Objective: Safety and quality of the track maximize safety and ride comfort minimize costs caused by damage to trains and track components due to bad track condition minimize penalties that may result from these Cost is proportional to how much measured values lie above the thresholds  Both safety and ride comfort are difficult to measure  Safety is not optimized but guaranteed, so this is modeled as a constraint and not an objective

49 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Cost (tamping and renewal cost) is defined by: Cost is subject to minimization:

50 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Calculation of train delays: Maximal admissible speed can be obtained from (predicted) track quality using, e.g., EN : Delay is to be minimized:

51 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS In this approach, the constraints handle important parts: Ensuring that the speed the trains go with is admissible given the tamping and renewal actions: Machine limits (of overall tamping and renewal that can be performed):

52 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Degradation model from D4.1: Tamping effect model from D4.2: Derivative of sigH is constant over tamping operations:

53 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Renewal effect model: Both and are set to values that can be considered “as good as new“ Renewal is always programmed in a whole section k Tamping and renewal are exclusive. Renewal has higher priority

54 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Initialization of the solutions Problem has very high dimensionality and complexity An intelligent initialization can be performed and is necessary  Program operations in first trimester where necessary  Calculate the remaining tamping capability up to the established threshold. Generate a random number r between 0 and this remainder.  Sort the track segments that have no tamping scheduled according to decreasing quality. Include tamping along this list up to the computed limit.  From this list, choose for each segment randomly if tamping will be performed or not.  Do the same for renewal  Apply deterioration and effect models, and begin from start iteratively for all trimesters

55 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Parameters for the Sweden case study: Sections ij are statically segmented, 5km each Segments k are dynamic segmentation from D4.1 Values for “as good as new“ (see also D4.2):

56 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results 500,000 evaluations of the fitness function, 104 solutions in the initial population

57 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Results (2) AMOSA yields better results than NSGA2: the problem is complex and high dimensional, and the initialization already uses a lot of problem-specific knowledge. AMOSA favors local search instead of exploration Best solutions with AMOSA: Cost (€) Delay (hours) TampingsRenewals …………

58 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Conclusions D4.4: Possibilities for optimization have been analysed in depth, and a framework was developed We implemented a state-of-the-art model: Multi-objective optimization minimizing cost and train delays, using the degradation model from D4.1 and the tamping effect model from D4.2 Complexity and high dimensionality is a big problem Intelligent initialization helps to cope with this problem However, with current resources, simplifications have to be made (regarding granularity of planning in time and space) Scheduling is another problem not touched here. However, this is necessary to define realistic benefits regarding transport and fixed costs

59 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS More conclusions: --> Track is so bad that tamping doesn't lift it anymore above threshold --> Renewal has to be programmed --> Important how cost of tamping and renewal relate to each other. Currently, 20 tampings cost the same as one renewal --> With a planning horizon of 3 years, the situation that a renewal saves cost will not occur --> With longer horizons, complexity even bigger, and deterioration model unreliable

60 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Conclusions

61 OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS We have developed methodologies for modeling of infrastructure and maintenance operations data, and we have shown how a predictive maintenance plan could be generated We have shown how data and expert knowledge can be used to achieve the OPTIRAIL goals, i.e., predict maintenance operations. We have adapted and implemented two multiple-objective algorithms for maintenance decision making


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