Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault detection accuracy and efficiency were compared. The comparative analysis showed that the random forest (RF) and support vector machine (SVM) could make full use of the CNN feature extraction ability.

Automated summary

Bearing fault diagnosis is crucial in aerospace, marine, and heavy industries for improving machine life, reducing economic losses, and preventing safety problems. Traditional methods for fault feature extraction face challenges, while deep neural networks can automatically extract intrinsic features. This study built four hybrid models based on CNN and compared their fault detection accuracy and efficiency. The results showed that random forest and support vector machine can maximize the CNN feature extraction ability.