An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction

Author:

He Fengxia1ORCID,Zheng Chuansheng1,Pang Chao2,Zhao Chengying1,Yang Mingyang2,Zhu Yunpeng3,Luo Zhong2ORCID,Luo Haitao4ORCID,Li Lei2,Jiang Haotian5

Affiliation:

1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China

2. School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

3. School of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK

4. Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

5. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China

Abstract

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.

Funder

Natural Science Foundation of Liaoning Province

National Natural Science Foundation of China

Central Government Guides Local Fund for Scientific and Technological Development in Liaoning Province

Basic Research Project of Shenyang Institute of Automation, CAS

Independent Project of State Key Laboratory of Robotics

Application of Dynamical Analysis and Sensing Technologies in Nondestructive

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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