Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet.

Shih-Lin Lin

doi:10.3390/s21227467

Abstract

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

Highlights

With the development of modern machinery and equipment, the structure of equipment has become more complex
Rolling bearing fault diagnosis is the process of determining the damage state through detection, isolation, and identification through data collected by the health monitoring of the rolling bearing
This paper combines the requirements of bearing fault diagnosis and the characteristics of monitoring signals and attempts to introduce the existing deep neural network recognition model into bearing fault diagnosis

Summary

Introduction

With the development of modern machinery and equipment, the structure of equipment has become more complex. With the emergence and widespread application of advanced technologies such as sensors, big data, and the Internet of Things, the development trend of mechanical fault diagnosis technology is bound to be combined with contemporary cutting-edge technologies. The early fault diagnosis method of rolling bearing was relatively simple, mainly through some statistical parameters (average value, root mean square value, kurtosis, etc.) to judge the fault condition of rolling bearing. These statistical values cannot determine the noise and interference caused by shaft speed changes, gears, and other vibration sources. Mechefske et al [5]

Methods

Results

Conclusion