Abstract
Many deep learning models for fault diagnosis have not considered the prior diagnosis knowledge of the rolling bearing. Moreover, some measuring locations cannot collect adequate data to diagnose due to equipment size or installation space problems. This paper proposes a wavelet convolutional deep transfer learning model for rolling bearing fault detection on cross-measurement points. A new convolution layer includes a redesigned convolution kernel and a new energy pooling layer. The convolution kernel is designed based on wavelet construction for mining time-frequency characteristics. The energy pooling layer is proposed to extract the energy of different frequency bands. The associated location's fault information has been transferred to domain knowledge to enhance the target features. Different domain features have adaptive matches based on multiple kernel variants of maximum mean discrepancy. The experimental results demonstrate that the accuracy of fault detection can reach 99.73%, and the robustness of the proposed method is also verified.
Highlights
Rolling bearings are essential components of rotating machinery [1]
The data in the table are the average diagnostic accuracy of the four rolling bearing types. It can be seen in the table that the average accuracy of all transfer tasks of the proposed method exceeds that of other methods by more than 20%, which shows that the proposed model can obtain good performance improvement from the improved wavelet convolution kernel
This paper proposes a fault detection method for rolling bearings to solve the problem of bearing fault detection on mechanical equipment that cannot directly obtain status data
Summary
Rolling bearings are essential components of rotating machinery [1]. Bearing failure diagnosis is crucial for equipment health [2]. Shao et al proposed an adversarial domain adaptation method based on deep transfer learning This approach used a deep residual network to process a timefrequency image to achieve cross-domain diagnosis [11]. Wen et al proposed a three-layer Sparse Auto-Encoder (SAE) to extract the features from raw data, and the MMD term was applied to minimize the discrepancy penalty of two domains This encoder was proven effective when transferred between multiple locations [12]. 3) The cross-domain diagnosis method based on a CNN converts one-dimensional (1D) signals into image data and uses the model in image recognition for classification. Such methods require a preprocessing stage and the insufficient consideration of the original vibration signal.
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