Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network

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Abstract
Low-speed bearing fault signal is weak and overwhelmed by background noise, and it is ineffective in diagnosing bearing faults. In this study, an improved statistical filtering and convolution neural network (CNN) low-speed bearing fault diagnosis method is presented. Statistical filtering is a convenient and effective signal-filtering method that can extract fault signals from strong background noise original signals. The improved method can deal with drawbacks in selecting the standard distinction index of statistical filtering algorithm. CNN is used to extract signal features automatically and build the diagnosis model for fault classification. An engineering experiment is performed with different condition-bearing fault signals to test this presented method, and the results show that the improved statistical filter and CNN are effective in diagnosing low-speed bearing signals. Comparison with traditional methods proves that this study has superior performance in low-speed bearing fault diagnosis.

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