Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH
Adaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filt...
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Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2019/7585401 |
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doaj-2d2b3c77ae1143a291038a2f544805462020-11-25T01:32:31ZengHindawi LimitedShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/75854017585401Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCHYanfeng Peng0Junhang Chen1Yanfei Liu2Junsheng Cheng3Yu Yang4He Kuanfang5Guangbin Wang6Yi Liu7Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, ChinaDepartment of Mechatronics, Foshan University, Foshan, Guangdong 528000, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaZhuzhou National Innovation Railway Technology Co., Ltd, Zhuzhou 412000, ChinaAdaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. The optimized objective function is a regulated singular local linear operator so that each obtained component is limited to be a LNB signal. Afterward, a component is generated by filtering the original signal with the optimized filter. Compared with MP, ASNBD is superior in both the physical meaning and the adaptivity. Drawbacks in EMD such as end effect and mode mixing are reduced in the proposed method because the application of interpolation function is not required. To achieve the fault diagnosis of roller bearings, raw signals are decomposed by ASNBD at first. Then, appropriate features of the decomposed results are chosen by applying distance evaluation technique (DET). Afterward, different faults are recognized by utilizing maximum margin classification based on flexible convex hulls (MMC-FCH). Comparisons between EMD and ASNBD show that the proposed method performs better in the antinoise performance, accuracy, orthogonality, and extracting the fault features of roller bearings.http://dx.doi.org/10.1155/2019/7585401 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanfeng Peng Junhang Chen Yanfei Liu Junsheng Cheng Yu Yang He Kuanfang Guangbin Wang Yi Liu |
spellingShingle |
Yanfeng Peng Junhang Chen Yanfei Liu Junsheng Cheng Yu Yang He Kuanfang Guangbin Wang Yi Liu Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH Shock and Vibration |
author_facet |
Yanfeng Peng Junhang Chen Yanfei Liu Junsheng Cheng Yu Yang He Kuanfang Guangbin Wang Yi Liu |
author_sort |
Yanfeng Peng |
title |
Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH |
title_short |
Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH |
title_full |
Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH |
title_fullStr |
Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH |
title_full_unstemmed |
Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH |
title_sort |
roller bearing fault diagnosis based on adaptive sparsest narrow-band decomposition and mmc-fch |
publisher |
Hindawi Limited |
series |
Shock and Vibration |
issn |
1070-9622 1875-9203 |
publishDate |
2019-01-01 |
description |
Adaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. The optimized objective function is a regulated singular local linear operator so that each obtained component is limited to be a LNB signal. Afterward, a component is generated by filtering the original signal with the optimized filter. Compared with MP, ASNBD is superior in both the physical meaning and the adaptivity. Drawbacks in EMD such as end effect and mode mixing are reduced in the proposed method because the application of interpolation function is not required. To achieve the fault diagnosis of roller bearings, raw signals are decomposed by ASNBD at first. Then, appropriate features of the decomposed results are chosen by applying distance evaluation technique (DET). Afterward, different faults are recognized by utilizing maximum margin classification based on flexible convex hulls (MMC-FCH). Comparisons between EMD and ASNBD show that the proposed method performs better in the antinoise performance, accuracy, orthogonality, and extracting the fault features of roller bearings. |
url |
http://dx.doi.org/10.1155/2019/7585401 |
work_keys_str_mv |
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