Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Shih-Lin Lin

doi:10.3390/electronics10182266

Abstract

In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.

Highlights

Mechanical fault diagnosis technology involves the monitoring, diagnosis, and early warning of the status and faults of continuously operating mechanical equipment
With a focus on the problem that the generalization ability of the diagnostic model decreases due to the variable working conditions of the motor, this paper proposed a rolling motor bearing cross-domain fault diagnosis method based on a medium Gaussian support vector machine (SVM)
The study found that the medium Gaussian has a better classification result in the fault diagnosis of the motor

Summary

Introduction

Mechanical fault diagnosis technology involves the monitoring, diagnosis, and early warning of the status and faults of continuously operating mechanical equipment. It is a science and technology to ensure the safe operation of machinery and equipment. It is a new discipline that has developed rapidly in recent years with the help of modern technological achievements in multiple disciplines. Rolling bearings are one of the important components of rotating machinery and equipment. The quality of its running state is directly related to the running state of the rotating equipment. The research on real-time monitoring and fault diagnosis of the working conditions of rolling bearings has received increasing attention from researchers. The current research literature and current situation are explained in the subsequent section

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Conclusion