FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm

Zihe Zhu, Zuolu Wang, Hao Zhang, Zhanqun Shi, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Because of the complex structure and bad working environment, the vibration signal of the planetary gear transmission system has strong noise. In mechanical fault diagnosis, the feature extraction of weak signal with strong noise is always a difficult problem. The sparse decomposition algorithm proposed in recent years can solve this problem to some extent. However, in the process of sparse decomposition, a large number of inner product calculations have been involved, which have the disadvantages of large computation and low efficiency. In this paper, the generalized orthogonal matching pursuit algorithm is applied to optimize the computation process of matching pursuit algorithm in order to reduce the amount of computation in operation process and improve the real-time performance in the process of fault diagnosis. It is proved that the generalized orthogonal matching tracking method can solve the shortcoming of large computation to a certain extent, and will not affect the reconstruction precision of the fault signal.
Original languageEnglish
Title of host publication15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018
Pages533-541
Number of pages9
Publication statusPublished - 2018
Event15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018 - Nottingham, United Kingdom
Duration: 10 Sep 201812 Sep 2018

Conference

Conference15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018
CountryUnited Kingdom
CityNottingham
Period10/09/1812/09/18

Fingerprint

Gears
Decomposition
Failure analysis
Feature extraction

Cite this

Zhu, Z., Wang, Z., Zhang, H., Shi, Z., & Gu, F. (2018). FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm. In 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018 (pp. 533-541)
Zhu, Zihe ; Wang, Zuolu ; Zhang, Hao ; Shi, Zhanqun ; Gu, Fengshou. / FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm. 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018. 2018. pp. 533-541
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title = "FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm",
abstract = "Because of the complex structure and bad working environment, the vibration signal of the planetary gear transmission system has strong noise. In mechanical fault diagnosis, the feature extraction of weak signal with strong noise is always a difficult problem. The sparse decomposition algorithm proposed in recent years can solve this problem to some extent. However, in the process of sparse decomposition, a large number of inner product calculations have been involved, which have the disadvantages of large computation and low efficiency. In this paper, the generalized orthogonal matching pursuit algorithm is applied to optimize the computation process of matching pursuit algorithm in order to reduce the amount of computation in operation process and improve the real-time performance in the process of fault diagnosis. It is proved that the generalized orthogonal matching tracking method can solve the shortcoming of large computation to a certain extent, and will not affect the reconstruction precision of the fault signal.",
author = "Zihe Zhu and Zuolu Wang and Hao Zhang and Zhanqun Shi and Fengshou Gu",
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Zhu, Z, Wang, Z, Zhang, H, Shi, Z & Gu, F 2018, FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm. in 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018. pp. 533-541, 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018, Nottingham, United Kingdom, 10/09/18.

FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm. / Zhu, Zihe; Wang, Zuolu; Zhang, Hao; Shi, Zhanqun; Gu, Fengshou.

15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018. 2018. p. 533-541.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Because of the complex structure and bad working environment, the vibration signal of the planetary gear transmission system has strong noise. In mechanical fault diagnosis, the feature extraction of weak signal with strong noise is always a difficult problem. The sparse decomposition algorithm proposed in recent years can solve this problem to some extent. However, in the process of sparse decomposition, a large number of inner product calculations have been involved, which have the disadvantages of large computation and low efficiency. In this paper, the generalized orthogonal matching pursuit algorithm is applied to optimize the computation process of matching pursuit algorithm in order to reduce the amount of computation in operation process and improve the real-time performance in the process of fault diagnosis. It is proved that the generalized orthogonal matching tracking method can solve the shortcoming of large computation to a certain extent, and will not affect the reconstruction precision of the fault signal.

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Zhu Z, Wang Z, Zhang H, Shi Z, Gu F. FAULT diagnosis of planetary gear drive system based on sparse decomposition optimized by generalized matching pursuit algorithm. In 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018. 2018. p. 533-541