Bearing overheating and anomalous accelerations are two principal failure modes for this safety component. The supervision of bearing’s behaviour is essential to ensure a safe and reliable operation. A safety component’s failure may cause a speed limitation or even a non-available train for operating, so a predictive maintenance for bearings and other critical components is mandatory for the manufacturers, operators and maintainers in the railway sector. Bearing temperature, exterior temperature, train speed and other variables are measured every second in real time. From all the data collected and stored in the last years some algorithms and models are designed and trained in this paper to detect bearing anomalies 2 days before a real failure is detected and the safety alarm is enabled. The methodology for obtaining the optimal algorithm is exposed. Different artificial neural networks based on different optimization models such as the Mini-batch Gradient Descent (MGD) or Adam optimizer are compared. A final neural network with 10 hidden layers to detect bearing failure is proposed reaching 99% of accuracy, 95% of precision and 90% of sensitivity. The objective of predicting a bearing anomaly with some days in advanced is reached with high precision level, which will lead also to cost savings and a contribution for the sustainability because many inspections could be reduced and the energy cost associated to them.

Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s2 is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s2). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.

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