Deep exponential excitation networks: toward stronger attention mechanism for weak fault diagnosis

Abstract

Considering that large mechanical equipment often has various excitation sources, the signals generated by these excitation sources are often not simply added or multiplied together, but nonlinearly mixed, which exhibit complex non-stationary characteristics, making classical algorithms difficult to extract fault features. Especially when faults just occur, the fault symptom is often weak and submerged by noise, resulting in low diagnosing accuracy. Accordingly, this article develops a new deep attention method, namely deep exponential excitation networks, which improves diagnosing performance by amplifying important information, that is, the discriminative information between normal and weak fault conditions. The developed method introduces an exponential function into the attention mechanism, yielding a stronger focus on important features. Here, our “stronger” means that the attention mechanism has larger weights, and wider weight ranges, which are achieved via three paradigms of exponential excitation blocks. Meanwhile, the weights are automatically learned in deep networks, which can adaptively amplify the information related to early weak faults according to different severities of faults. Finally, extensive experiments on the marine engine datasets containing different noises demonstrate the effectiveness of the developed method.