Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminatin...

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Autores principales:	Di Xu, Jianghua Ge, Yaping Wang, Junpeng Shao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-frequency weak signal adaptive cascaded stochastic resonance system quantum particle swarm optimization variational mode decomposition rolling bearing Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/dd851e11a77545279867c772b7244e76
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spelling	oai:doaj.org-article:dd851e11a77545279867c772b7244e762021-11-25T18:12:15ZMulti-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance10.3390/machines91102752075-1702https://doaj.org/article/dd851e11a77545279867c772b7244e762021-11-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/275https://doaj.org/toc/2075-1702In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.Di XuJianghua GeYaping WangJunpeng ShaoMDPI AGarticlemulti-frequency weak signaladaptive cascaded stochastic resonance systemquantum particle swarm optimizationvariational mode decompositionrolling bearingMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 275, p 275 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-frequency weak signal adaptive cascaded stochastic resonance system quantum particle swarm optimization variational mode decomposition rolling bearing Mechanical engineering and machinery TJ1-1570
spellingShingle	multi-frequency weak signal adaptive cascaded stochastic resonance system quantum particle swarm optimization variational mode decomposition rolling bearing Mechanical engineering and machinery TJ1-1570 Di Xu Jianghua Ge Yaping Wang Junpeng Shao Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
description	In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.
format	article
author	Di Xu Jianghua Ge Yaping Wang Junpeng Shao
author_facet	Di Xu Jianghua Ge Yaping Wang Junpeng Shao
author_sort	Di Xu
title	Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
title_short	Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
title_full	Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
title_fullStr	Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
title_full_unstemmed	Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance
title_sort	multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dd851e11a77545279867c772b7244e76
work_keys_str_mv	AT dixu multifrequencyweaksignaldecompositionandreconstructionofrollingbearingbasedonadaptivecascadedstochasticresonance AT jianghuage multifrequencyweaksignaldecompositionandreconstructionofrollingbearingbasedonadaptivecascadedstochasticresonance AT yapingwang multifrequencyweaksignaldecompositionandreconstructionofrollingbearingbasedonadaptivecascadedstochasticresonance AT junpengshao multifrequencyweaksignaldecompositionandreconstructionofrollingbearingbasedonadaptivecascadedstochasticresonance
_version_	1718411529659875328

Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

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