Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance

The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal...

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Autores principales:	Bingbing Hu, Shuai Zhang, Ming Peng, Jie Liu, Shanhui Liu, Chunlin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	stochastic resonance underdamped asymmetric periodic potential local gear damage weak feature extraction Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a7d8513a792943eaab51a230b13ceec6
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spelling	oai:doaj.org-article:a7d8513a792943eaab51a230b13ceec62021-11-25T19:05:59ZWeak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance10.3390/sym131120082073-8994https://doaj.org/article/a7d8513a792943eaab51a230b13ceec62021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2008https://doaj.org/toc/2073-8994The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal when demodulating the fault signal. However, the traditional bistable stochastic resonance (BSR) system cannot accurately match the asymmetric characteristics of the envelope signal because of its symmetrical potential well, which weakens the detection performance for weak faults. In order to overcome this problem, a novel method based on underdamped asymmetric periodic potential stochastic resonance (UAPPSR) is proposed to enhance the weak feature extraction of the local gear damage. The main advantage of this method is that it can better match the characteristics of the envelope signal by using the asymmetry of its potential well in the UAPPSR system and it can effectively enhance the extraction effect of periodic impact signals. Furthermore, the proposed method enjoys a good anti-noise capability and robustness and can strengthen weak fault characteristics under different noise levels. Thirdly, by reasonably adjusting the system parameters of the UAPPSR, the effective detection of input signals with different frequencies can be realized. Numerical simulations and experimental tests are performed on a gear with a local root crack, and the vibration signals are analyzed to validate the effectiveness of the proposed method. The comparison results show that the proposed method possesses a better resonance output effect and is more suitable for weak fault feature extraction under a strong noise background.Bingbing HuShuai ZhangMing PengJie LiuShanhui LiuChunlin ZhangMDPI AGarticlestochastic resonanceunderdampedasymmetric periodic potentiallocal gear damageweak feature extractionMathematicsQA1-939ENSymmetry, Vol 13, Iss 2008, p 2008 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	stochastic resonance underdamped asymmetric periodic potential local gear damage weak feature extraction Mathematics QA1-939
spellingShingle	stochastic resonance underdamped asymmetric periodic potential local gear damage weak feature extraction Mathematics QA1-939 Bingbing Hu Shuai Zhang Ming Peng Jie Liu Shanhui Liu Chunlin Zhang Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
description	The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal when demodulating the fault signal. However, the traditional bistable stochastic resonance (BSR) system cannot accurately match the asymmetric characteristics of the envelope signal because of its symmetrical potential well, which weakens the detection performance for weak faults. In order to overcome this problem, a novel method based on underdamped asymmetric periodic potential stochastic resonance (UAPPSR) is proposed to enhance the weak feature extraction of the local gear damage. The main advantage of this method is that it can better match the characteristics of the envelope signal by using the asymmetry of its potential well in the UAPPSR system and it can effectively enhance the extraction effect of periodic impact signals. Furthermore, the proposed method enjoys a good anti-noise capability and robustness and can strengthen weak fault characteristics under different noise levels. Thirdly, by reasonably adjusting the system parameters of the UAPPSR, the effective detection of input signals with different frequencies can be realized. Numerical simulations and experimental tests are performed on a gear with a local root crack, and the vibration signals are analyzed to validate the effectiveness of the proposed method. The comparison results show that the proposed method possesses a better resonance output effect and is more suitable for weak fault feature extraction under a strong noise background.
format	article
author	Bingbing Hu Shuai Zhang Ming Peng Jie Liu Shanhui Liu Chunlin Zhang
author_facet	Bingbing Hu Shuai Zhang Ming Peng Jie Liu Shanhui Liu Chunlin Zhang
author_sort	Bingbing Hu
title	Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
title_short	Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
title_full	Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
title_fullStr	Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
title_full_unstemmed	Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
title_sort	weak feature extraction of local gear damage based on underdamped asymmetric periodic potential stochastic resonance
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a7d8513a792943eaab51a230b13ceec6
work_keys_str_mv	AT bingbinghu weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance AT shuaizhang weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance AT mingpeng weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance AT jieliu weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance AT shanhuiliu weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance AT chunlinzhang weakfeatureextractionoflocalgeardamagebasedonunderdampedasymmetricperiodicpotentialstochasticresonance
_version_	1718410297290522624

Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance

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