Rolling bearing fault diagnosis model based on DSCB-NFAM

Abstract

Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on depth-separable convolutional block (DSCB)-non-local feature awareness module (NFAM) is proposed. Firstly, a DSCB is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a NFAM is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

Zero-shot fault diagnosis of high-voltage circuit breakers: fusion of phase space reconstruction and attribute embedding methods

“…The AEZSD method was compared with other state-of-the-art zero-shot diagnosis methods, as shown in table 5. For comparison with traditional supervised learning methods, we selected four methods: DSCB-NFAM [23], NI-CNN [24], CS-DCNN [25], and TBLS [26]. Since traditional methods are not designed for zero-shot fault diagnosis, we re-partitioned the dataset by randomly selecting 70% of each fault type's data for training and the remaining 30% for testing.…”