Time varying filter empirical mode decomposition and MobileNet-V3 algorithms for fault diagnosis of rolling bearing with efficient channel attention mechanism

Abstract

Rolling bearings are the core components of rotating mechanical equipment, and it is more and more important to fault diagnosis for the operation state and safety of the mechanical equipment. Deep learning has been widely applied to fault diagnosis of rolling bearings, but it is limited with the high complexity of deep neural networks and the dependence on hardware resources. Time varying filter empirical mode decomposition (TVFEMD) method based on Subtraction-Average-Based Optimizer (SABO) optimization is applied to the subtle feature extraction of rolling bearings. Improved MobileNetV3-Large lightweight model based on Exponential Linear Units (ELU) activation function and Efficient Channel Attention (ECA) mechanism is proposed to fault diagnosis. TVFEMD-MobileNet-V3 algorithm with lightweight property is built to subtle fault diagnosis and recognition of rolling bearings. It has been validated on the bearing dataset from Case Western Reserve University (CWRU) with 99.28% accuracy and 3.57 M reference count. The anti-noise performance of TVFEMD-MobileNet-V3 has been verified, and the average accuracy is 93.83% when signal-to-noise ratio is 2 dB. TVFEMD-MobileNet-V3 algorithm has great potential to subtle feature recognition with limited sample sizes of the rolling bearing.

Keywords

Bearing fault diagnosis MobileNetV3-large time varying filtering empirical mode decomposition efficient channel attention mechanism deep learning

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