Multiscale Noise Reduction Attention Network for Aeroengine Bearing Fault Diagnosis

Aeroengine has a complex mechanical structure, and its working condition varies widely. Its fault signals are thus modulated through complex nonlinear transfer paths and influenced by non-Gaussian noises. The popular data-driven multiscale diagnostic model, however, does not sufficiently consider the embedded noises and consequently keeps the noise content in the advanced discriminative features, which decreases the diagnostic accuracy. Therefore, a multiscale attention network with adaptive noise reduction (MANANR) is proposed in this article for aeroengine bearing fault diagnosis. The MANANR first divides the original vibration signal into different scales via the average method of adjacent points. Then, two stacked multiscale noise reduction modules, B1 and B2, are designed for noise reduction. The core strategy behind B1 and B2 is the threshold noise reduction (TNR), which removes the noises from multiscale convolution features adaptively. Based on the physical principles that global average pooling (GAP) and max pooling (MAP) can extract periodic and impulsive characteristics of the fault signals respectively, the threshold value of TRN is thus constructed through fusing GAP and MAP outputs. Furthermore, an attention mechanism is employed to enhance the discriminative capability of multiscale features by globally capturing the relations among different scales and channels. Finally, one two-layer classifier is introduced to confirm bearing fault patterns. Experimental results demonstrate that the proposed method has the feature-level noise reduction property, and more importantly, achieves satisfying intelligent diagnosis precision for aeroengine bearing with the minimum peeling area of 0.5 mm2 under the continuous acceleration condition from 12 000 to 12 550 r/min, which outperforms state-of-the-art intelligent diagnostic methods.