Bearing Fault Diagnosis Based on a Novel Adaptive ADSD-gcForest Model

Abstract

With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning is often determined by trial and error, which is time-consuming and lacks theoretical support. To address the above problems, an adaptive (Adaptive Depthwise Separable Dilated Convolution and multi-grained cascade forest) ADSD-gcForest fault diagnosis model is proposed in this paper. Multiscale convolution combined with convolutional attention mechanism (CBAM) concentrates on effectively extracting fault information under strong noise, and the Meta-Activate or Not (Meta-ACON) activation function is integrated to adaptively optimize the model structure according to the characteristics of input samples, then gcForest outputs the final diagnosis result as the classifier. The experiment compares the effects of three bearings failure diagnoses under various noise and load conditions. The experimental results show the effectiveness and practicability of the proposed method.