Abstract

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

Highlights

  • Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

  • As industrial automation becomes increasingly popular, motors are used in various mechanical systems to supply power

  • Fault diagnosis and detection are essential for maintaining the high performance and reliability of the entire mechanical system [1]

Read more

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As industrial automation becomes increasingly popular, motors are used in various mechanical systems to supply power. The advantage of automation is that it makes production lines faster and more flexible. Faults in the motors and machine elements, including bearings, gearboxes, and shafts, may result in substantial financial costs and human safety problems. Fault diagnosis and detection are essential for maintaining the high performance and reliability of the entire mechanical system [1]. Fault diagnosis can prevent unexpected lengthy process shutdowns, damage to the mechanical system, unnecessary maintenance operations, and even expensive repairs. To prevent catastrophic motor failure, early fault diagnosis of the motor and machine elements is important

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call