Sparsity-Oriented Nonconvex Nonseparable Regularization for Rolling Bearing Compound Fault under Noisy Environment

Xiaocheng Li,Hongyuan Wang,Jingcheng Wang

doi:10.1155/2020/8823102

Xiaocheng Li, Hongyuan Wang + Show 1 more

Open Access

https://doi.org/10.1155/2020/8823102

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Abstract

Rolling bearing is widely used in rotating machinery and, at the same time, it is easy to be damaged due to harsh operating environments and conditions. As a result, rolling bearing is critical to the safe operation of the machinery devices. Compound fault of rolling bearing is not a simple superimposition of multiple single faults, but the coupling of multiple fault features, making the vibration signal, becomes complicated. In our study, sparsity-oriented nonconvex nonseparable regularization (SONNR) method is proposed to rolling bearing compound fault diagnosis under noisy environment. Firstly, a theoretical model of rolling bearing compound fault is established, and the vibration characteristics of rolling bearing compound fault are analyzed. Secondly, four-layer structure of the SONNR method is proposed: input layer, nonconvex sparse regularization layer, signal reconstruction layer, and compound faults isolation layer. Finally, the validity of the method is verified by simulation data and actual data, and it is compared with the traditional time domain diagnostic methods and artificial intelligence methods.

Highlights

Rolling bearing is widely used in many mechanical devices
The vibration signal generated by a single fault of the rolling bearing has been extensively studied and a series of powerful diagnostic methods have been made
(4) We present a verification based on real damage data set to validate the effectiveness of the proposed method in the diagnosis of compound faults in rolling bearing

Summary

Introduction

Rolling bearing is widely used in many mechanical devices. With increase in complexity of machinery, the requirements of reliability for rolling bearing are increasing. E inner ring acts to match the shaft and rotates around the shaft. The vibration signal generated by a single fault of the rolling bearing has been extensively studied and a series of powerful diagnostic methods have been made. In the course of the rolling bearing, there is single fault; with the change of the operating environment and running time increasing, bearing often has two or more faults that are named compound fault of the rolling bearing. Compound fault is not a simple superimposition of multiple single faults, but the coupling of multiple fault features, making the vibration signal complicated. Rolling bearings are often disturbed by various background noises during operation. In order to verify the effectiveness of the method, we artificially added Gaussian white noise. In order to verify the effectiveness of the method, we artificially added Gaussian white noise. erefore, the study of compound fault signals detection under noisy environment technologies becomes urgent to overcome

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Conclusion