AM-FM Operator Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis

HUANG Wu; ZHENG Jinde; TONG Jinyu; PAN Haiyang; LIU Qingyun

doi:10.13433/j.cnki.1003-8728.20230019

Volume 43 Issue 7

Jul. 2024

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2024 > 43(7): 1257-1265

HUANG Wu, ZHENG Jinde, TONG Jinyu, PAN Haiyang, LIU Qingyun. AM-FM Operator Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(7): 1257-1265. doi: 10.13433/j.cnki.1003-8728.20230019

Citation:

HUANG Wu, ZHENG Jinde, TONG Jinyu, PAN Haiyang, LIU Qingyun. AM-FM Operator Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(7): 1257-1265. doi: 10.13433/j.cnki.1003-8728.20230019

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AM-FM Operator Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis

doi: 10.13433/j.cnki.1003-8728.20230019

School of Mechanical Engineering, Anhui University of Technology, Ma′anshan 243032, Anhui, China

Received Date: 2022-04-08
Publish Date: 2024-07-25

Abstract

Abstract

The operator-based null-space tracking algorithm can realize the adaptive decomposition of complex signals, and it is a key step to construct and solve the signal model. A new signal decomposition model based on AFO is further established by defining a new AM-FM operator (AFO) which can completely annihilate AM-FM signals. In order to improve the robustness of parameters to signal decomposition, a nonparametric regularization (NPR) method is used to solve the constrained optimization problem of the above models, and an NPR-based adaptive signal decomposition method, called NPR-AFO, is proposed. This paper introduces the NPR-AFO method into mechanical fault diagnosis, and compares it with other existing decomposition methods through simulation and analysis of the measured data of local faults of rolling bearings. The results show that the proposed method can not only effectively extract fault features, but also the state failure characteristics are more obvious.

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References(24)

References

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