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.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

“…Therefore, an important core technology of smart machinery is how to improve equipment activation through intelligent sensing and fault diagnosis prediction, and how to reduce the risk of downtime due to equipment failure. There are many types of mechanical equipment failures, such as abrasion and damage of the processing machine's spindle cutter [1][2][3][4][5][6][7], the abnormal damage of the bearing [8][9][10][11][12] or gearbox of the rotary machinery due to the harsh environment, rotational instability caused by mechanical failures of the power generator, etc. Therefore, accurate prediction of mechanical failures will reduce production losses, a key factor, and condition for the efficient production of smart machinery.…”

Special Issue: Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control

“…Zhai et al [9] propose an adaptive depth-wise separable dilated convolution and multigrained cascade forest (ADSD-gcForest) fault diagnosis model for fault diagnosis in bearings. The multiscale convolution, combined with the convolutional attention mechanism, 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.…”