Fault diagnosis of bearing based on refined piecewise composite multivariate multiscale fuzzy entropy

As one of the key components of the train, the condition of the bearing is related to the train's safe operation. The vibration signal of the bearing is usually nonlinear and nonstationary, which makes it difficult to extract fault features and leads to low diagnostic accuracy. Entropy theory can effectively measure the change of nonlinearity and complexity of the vibration signal. However, the conventional entropy algorithm cannot extract the characteristics of a multi-channel signal simultaneously. A bearing fault diagnosis method based on refined piecewise composite multivariate multiscale fuzzy entropy (RPCMMFE) and convolutional neural network (CNN) is proposed. The proposed method can fully extract fault features and improve fault diagnosis accuracy. Firstly, based on multivariate multiscale fuzzy entropy (MMFE), RPCMMFE is proposed by introducing refined theory and piecewise composite theory. Then, the RPCMMFE of different fault signals is calculated, and RPCMMFE is considered to be a feature vector that acts as an input into the CNN for fault diagnosis. Finally, it is verified by two groups of experiments. The experimental results show that the features extracted by this method have better stability and discrimination ability and can identify bearing faults more accurately. The average accuracy rates are 99% and 99.17%, respectively.(c) 2022 Elsevier Inc. All rights reserved.