Time consideration in machine learning models for train comfort prediction using LSTM networks

Abstract

Safety and passengers’ comfort affect directly the reliability and availability of trains. Therefore, appropriate monitoring of the variables influencing those two concepts is mandatory for good operation and maintenance costs reduction. Moreover, operators and rail manufacturers are evolving to a condition-based maintenance strategy (CBM). For this purpose, machine learning is being used to predict early main component’s failures, avoiding a loss of reliability or availability during commercial operation. From the passengers’ point of view, punctuality and comfortability are two of the most relevant factors when evaluating a railway transport service. Comfort is mainly based on the availability of all services in the train, but it is also related to the accelerations received through the car body to the passengers. More precisely, the lateral car body acceleration (Ayc variable in this paper) is a key variable related to comfort perception and it is therefore important to be monitored and controlled. Rolling, suspension, and damping elements must be kept in perfect conditions during operation to achieve a smooth train movement. In this paper, an optimized model based on Long Short Term Memory networks (LSTM) is designed and trained to predict lateral car body accelerations using only the train accelerometers and with no additional sensoring for this specific experiment. This model will be compared with artificial neural networks (ANN) models to evaluate the relevance of time dependency for this purpose. The optimal time frame to group data is a key characteristic of the neural network training process and is also determined in this paper. The model is trained with data corresponding to the correct dynamic behavior of the train. To test the reliability of this method, a real case of excessive train movement is tested. In these situations, an alert could be sent to the operator or the maintainer for a not-programmed maintenance inspection at the depot (predictive maintenance), avoiding future passenger complaints for the loss of comfort or, in extreme cases, speed limitations due to safety reasons for excessive accelerations. The main contributions of this work are the following:- A LSTM-based neural network, predicting lateral car body accelerations achieving better results than a plain ANN for this dataset: 0.0335 m/s2 MAE and a 12% gain on extreme axles.- An optimization of the time frame to group the measurements in this algorithm that is considering as an input the time variable: 5 s.- A real use case of the model in which monitoring the deviation would have helped to take an early maintenance adjustment on the suspension or damping elements to prevent the loss of comfort.- The p-value used to reduce the uncertainty of the results obtained in the real use case is reaching over 0.95 in the coaches with good dynamic behavior and less than 0.15 in the coaches where a dynamic problem related to comfort loss is detected.