Bearing Fault Diagnosis Based on Adaptive Denoise Residual Network with Image Features of Vibration Signals
In order to solve the problem that the bearing diagnosis model has poor efficiency and anti-noise performance for the one-dimensional original bearing vibration signal input,a bearing fault diagnosis method based on adaptive denoise residual network with vibration signals' image feature was proposed.In this method,the one-dimensional bearing vibration signal was truncated and overlap-sampled,then reconstructed into a signal matrix,and finally encoded into an image to obtain a vibration signal image.The histogram processing was used to process the images to obtain a grayscale distribution feature matrix.The vibration signal image and the its grayscale distribution feature matrix were used as the input of the algorithm model.And a denoise path based on the channel attention mechanism was inserted into the process of residual convolution mapping in the proposed model,and the threshold for denoising was obtained adaptively.Finally,the fault feature extraction performance of the network for noisy samples was improved.The comparative experiments show that the model after adding grayscale distribution feature has better performance;the proposed adaptive noise reduction residual network model still has high fault identification accuracy although the vibration signal containing noise is used as the input.
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