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Estimating the Uncertainty of Brake Pad Prognostics for High-Speed Rail with a Neural Network Feature Ensemble

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Estimating the Uncertainty of Brake Pad Prognostics for High-Speed Rail with a Neural Network Feature Ensemble Estimating the Uncertainty of Brake Pad Prognostics for High-Speed Rail with a Neural Network Feature Ensemble Alexandre Trilla1, Pierre Dersin2, and Xavier Cabre´3 1,3 ALSTOM, Santa Perpetua` de la Mogoda, Barcelona, 08130, Spain [email protected] [email protected] 2 ALSTOM, Saint Ouen, Paris, 93400, France [email protected] ABSTRACT The friction brake system reduces the speed of the train by transforming the kinematic energy into heat through the abra- sion between the carbon pads and the disk. The British Rail Class 390 fleet (Pendolino) features a very high availabil- ity, running 1000 miles a day on average, so their wear rate is monotonic and acceptably constant. The prognostics for brake pad degradation are typically conducted with a robust online linear regression technique, which seamlessly accom- modates asset-based idiosyncrasies, like the different effort that is exerted on the pad given its location on a motor or a trailer car, on the left or the right hand side of the caliper, etc. This technique is also resilient to abrupt measurement changes due to asset replacements, sensor imprecision, and acquisition failures, while retaining the physical evolution of Figure 1. ALSTOM TrainScanner deployment at the Manch- the wear, which erodes the surface of the pad. This article ester Traincare Centre. evaluates the effectiveness of this approach with a dataset of brake pad thickness measurements, at the fleet level (around 12000 asset instances), using a sliding window technique, her, K. and Dersin, P. and Lamoureux, B. and Zerhouni, N., and refines its performance with a neural network ensemble, 2017). In this regard, ALSTOM has developed the Train- which blends physical and location features. The results of Scanner, see Figure 1, which is a train monitoring system the analysis prove that this method meets the requirements of that is aimed at optimising the maintenance of brake pads, the maintenance staff and thus yields a new avenue for busi- pantograph carbon strips, and wheelsets, through the deploy- ness improvement through the application of the predictive ment of the PHM methodology and its associated techniques. maintenance approach for brake pads. TrainScanner integrates a series of acquisition subsystems with lasers and 3D cameras that capture the related measures 1. INTRODUCTION as a train traverses its portal. Then, it automatically conducts the processing and analysis of the collected data, and finally There exist many studies that review the advantages of the it triggers alarms and issues reports to the maintenance staff. PHM technology for the industry (Sikorska, J. Z. and Hod- This work is particularly focused on the brake pad prognos- kiewicz, M. and Ma, L., 2011). This work is especially con- tics that are attainable with the carbon thickness data provided cerned with the application of PHM to the maintenance of by TrainScanner over time. railway and rolling-stock assets (Atamuradov, V. and Medja- Brake pad prognostics have been initially approached with Alexandre Trilla et al. This is an open-access article distributed under the finite element method simulation (AbuBakar, A. R. and terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, pro- Ouyang, H., 2008), highlighting the importance to consider vided the original author and source are credited. the braking forces (Malvezzi, M. and Papini, S. and Pugi, L. 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2018 and Vettori, G. and Tesi, S. and Rindi, A. and Meli, E., 2013). Sliding window technique Other variables have also been incorporated to better estimate 45 the degradation, like the braking energy and the tempera- Brake pad thickness 40 Replacement, error... ture (Antanaitis, D. B. and Riefe, M. T., 2016), the braking History window action time and the vehicle route (Kreis, C. and Dobberphul, 35 Prediction horizon T., 2018), or the brake pad location (Jegadeeshwaran, R. and Prediction evaluation 30 Sugumaran, V., 2015). Other authors have focused on statisti- cal and histogram information to create a reference wear pro- 25 file and detect deviations (Chassefeyre, V., 2012) or diagnose 20 brake faults directly (Manghai, T. M. A. and Jegadeeshwaran, R. and Sugumaran, V., 2017). 15 Carbon pad thickness [mm] This work conducts a thorough analysis of brake pad wear at 10 the fleet level in order to quantify the uncertainty of the pre- 5 diction at 40000km into the operating life of the brake pad, 0 which is expected to stretch up to 350000km. The time it 0 50 100 150 200 250 300 350 takes the trains to run 40000km (around 20 days) is the no- Mileage [x1000 km] tice requested by the maintenance team in order to schedule the depot resources effectively. This prognosis evaluation is Figure 2. Evaluation of brake pad prognostics with the sliding window prediction technique. performed with a sliding window prediction technique, using regression techniques and neural networks (Hota, H. S. and Handa, R. and Shrivas, A. K., 2007). The article is organ- precision is 0.5mm. The prediction needs to be robust ised as follows: Section 2 describes the analysis procedure to this measurement variability that has been followed, including the description of the data, the evaluation technique, and the prognostic methods, along The resulting set of data is smooth and ready to be subject to with their preliminary results. Section 3 discusses the overall further modelling and analysis. results and the limitations of the approach, and Section 4 con- The British Rail Class 390 fleet (Pendolino) is composed of cludes the manuscript and reflects on its impact to the current 9-car trainsets, and 11-car trainsets, with 6 or 7 motor cars maintenance plan. respectively. Each motor car has two motor axles, and two trailer axles. For a trailer car, all axles are trailer. The most 2. METHODS AND RESULTS common braking operation combines the electrical braking This section describes the sequential process that has been force of the motor (obviously, this is only available on motor followed in order to obtain a robust brake pad prognostics axles), and the friction braking force of the pads, which are procedure. Thus, the development is incremental and prelim- available on all axles, but typically they are not used on motor inary results are provided. axles (their use is restricted to emergency braking, parking, etc). In addition, the pneumatic pressure applied to the vari- 2.1. Carbon Pad Data Preprocessing ous pads along the train is different, to compensate the con- tribution of these different technologies and attain a balanced This article evaluates the effectiveness of brake pad prognos- dynamic behaviour for all cars, regardless of the different car tics with a dataset of brake pad thickness measurements at weights, service load, speed, etc. The Class 390 Pendolino the fleet level, obtained with TrainScanner from November trains run a steady mission profile (i.e, the West Coast Main 1, 2016, to March 1, 2017. It comprises the evaluation of Line in the UK), which leads to expect a uniform degrada- 11836 brake pad assets. Each set of carbon pad thickness tion at the pad level. However, the aforementioned brake sys- measurements needs to be preprocessed to add robustness to tem differences also lead to expect differences at the car/axle the prediction. To this end, the following issues are taken into level. account: 1. Asset replacement: steep positive thickness increments 2.2. Sliding Window Prediction Evaluation (greater than 20mm) with a final value close to a new A rolling window is used on the continuum of clean carbon asset measure, i.e., 34mm, need to be segmented and thickness measurements in order to provide a history frame treated as different assets that is used to make a prediction, which is then evaluated 2. Acquisition failures: extreme values need to be regarded with the remaining points at a given horizon (Hota, H. S. and as invalid data and discarded from the analysis, such as Handa, R. and Shrivas, A. K., 2007), see Figure 2. Similar values out of pad range, zeroes, etc. approaches have also been derived using the uncertainty in- 3. Sensor precision: TrainScanner’s rated measurement tervals that surround the trend (Greitzer & Ferryman, 2001). 2 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2018 It is to note that according to the ISO 13374 standard (ISO, Brake pad prediction evaluation with linear regression 2003), which is our main PHM development guideline, this 4000 prediction effectiveness assessment should be conducted with Error (2.96mm) the Remaining Useful Life figures (i.e., the output of the 3500 Prognosis module) instead of the brake pad thickness mea- 3000 surements. However, the actual replacement record is not available due to the uncertainty between the asset replace- 2500 ment actions (which can be done in any depot) and the asset 2000 monitoring events (which is only available at Manchester). Therefore, we reframe the objective as a sequence prediction 1500 problem. Number of evaluations 1000 2.2.1. Estimation of Uncertainty 500 The specific statistical terms of “accuracy” and “precision” 0 are related with the difference between a real magnitude and −4 −3 −2 −1 0 1 2 3 4 a calculated value, both in terms of bias and variance error. Error value [mm] Its bias, also known as trueness (ISO, 1994), is of little impor- Figure 3. Histogram of the prediction error for a history win- tance in this work to evaluate the effectiveness of a predictive dow of 40000km with weighted online linear regression. In technique, because it can be easily corrected if it is known (or brackets, the estimated uncertainty. experimentally estimated) in advance, which is a side objec- tive of the evaluation techniques presented in this paper (the main use of bias is for detecting model underfitting).
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