Rotating machinery fault diagnosis based on optimized Hilbert curve images and a novel bi-channel CNN with attention mechanism

Abstract

Deep learning methods have been widely investigated in machinery fault diagnosis owing to their powerful feature learning capability. However, high accuracy is hard to achieve due to the limited fault information in a single domain when the data volume is small. In this paper, an optimized Hilbert curve (OHC) method is developed, which can generate a novel domain to highlight the fault impulses of vibration signals. To fully mine the fault information, a bidirectional-channel convolutional neural network with an attention mechanism is further proposed, in which two channels are constructed and a transmission channel selection is conducted by a novel improved convolutional block attention module. First, the OHC images and the time-frequency representations are obtained by OHC and wavelet transform respectively. Second, the two types of representations are fed into the channels respectively for feature learning. Finally, the learned features are allocated to different attention mechanism for feature fusion and classification. The proposed method is evaluated via the datasets of rolling bearings and planetary gearboxes, and results show that it outperforms the comparison methods.