An Efficient and Flexible Diagnostic Method for Machinery Fault Detection Based on Convolutional Neural Network
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In the field of feature extraction and machinery fault detection, intelligent fault diagnosis of rotating machinery has drawn much attention. This paper proposes an efficient and flexible diagnostic method based on convolutional neural network (CNN). The method directly feeds the original one-dimensional signals into the formulated network, and adopts one-dimensional convolution kernels to extract representative features. This reduces complexity and time consumption. In the training process, the stochastic gradient descent (SGD) method with momentum is adopted to minimize the loss function of the formulated learning network, so that it could get rid of local minimum points and saddle points as well as speed up optimizing. The experimental results demonstrate that the proposed method effectively identifies the rolling bearing faults under different conditions.
KeywordsRolling bearing Fault diagnosis Feature extraction Convolutional neural network Deep learning
This work is supported by the National Natural Science Foundation of China (61571346). The research is also supported by the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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