Application of deep learning for rolling stock failure prediction

This paper begins a series of publications, making a case and opportunity for creation of advanced diagnostics of technical condition of the railway rolling stock and provides essentially new artificial intelligence (AI) solution based on deep learning techniques. Here, the abstraction for anomalous data is formed, statements of industrial equipment failure detection and forecasting problems are set, core components of software implementation of the solution algorithm using the recurrent network (LSTM autoencoder) are described as well as the quality assessments and the reasoning for choosing deep learning over the rest AI methods.

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