SIGNAL (programming language)

Algorithm

Residual

Process (computing)

Engineering

Real-time computing

Operating system

Computer science

Fault (geology)

Seismology

Geology

Mechanical engineering

Pattern recognition (psychology)

Automotive engineering

Artificial intelligence

Structural engineering

Bogie

Programming language

Artificial neural network

Feature extraction

Convolutional neural network

IEEE Transactions on Industrial Informatics

Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of an HST. In prior works, feature extraction from multisensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or postprocessing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification.

Fault Diagnosis of High-Speed Train Bogie by Residual-Squeeze Net