Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing

Xiaoli Tang, Yuandong Xu, Fengshou Gu, Guangbin Wang

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

Abstract

Rolling element bearings are the essential components of rotating machines, faults of which can cause serious failures or even major breakdowns of a machine. Fault diagnosis deliveries significant benefits to machines with rolling element bearings by finding the faults at early period and taking corrective actions to enhance safe and high performance operations. However, multiple sensor usages and high rate data acquisition involved in a monitoring system have considerable drawbacks of high system cost involved in purchasing hardware for data transfer, storage and processing. To reduce these shortages, this paper investigates compressive sensing (CS) techniques for the fault detection of rolling element bearings. Based on the frequency shift and envelope analysis, a CS scheme is developed for monitoring the bearing. The number of data transmitted and stored can be reduced by several thousands of times. The simulation and the experimental results demonstrate that the compressed vibration signals of rolling element bearings are effective to detect bearing faults at the total compressing ratio up to several thousand with the corresponding maximum compression ratio (CR) of CS process at nearly 100. In addition, several performance measures are applied to evaluate the reconstructed signals and show approximately the information about the noise level of the system.
LanguageEnglish
Title of host publicationProceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9780701702601
ISBN (Print)9781509050406
DOIs
Publication statusPublished - 26 Oct 2017
Event23rd International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing - University of Huddersfield, Huddersfield, United Kingdom
Duration: 7 Sep 20178 Sep 2017
Conference number: 23
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=41042 (Link to Conference Website)

Conference

Conference23rd International Conference on Automation and Computing
Abbreviated titleICAC 2017
CountryUnited Kingdom
CityHuddersfield
Period7/09/178/09/17
OtherThe scope of the conference covers a broad spectrum of areas with multi-disciplinary interests in the fields of automation, control engineering, computing and information systems, ranging from fundamental research to real-world applications.
Internet address

Fingerprint

Bearings (structural)
Fault detection
Monitoring
Purchasing
Data transfer
Computer hardware
Failure analysis
Data acquisition
Sensors
Processing

Cite this

Tang, X., Xu, Y., Gu, F., & Wang, G. (2017). Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing. In Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2017.8082063
Tang, Xiaoli ; Xu, Yuandong ; Gu, Fengshou ; Wang, Guangbin. / Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing. Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 2017.
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abstract = "Rolling element bearings are the essential components of rotating machines, faults of which can cause serious failures or even major breakdowns of a machine. Fault diagnosis deliveries significant benefits to machines with rolling element bearings by finding the faults at early period and taking corrective actions to enhance safe and high performance operations. However, multiple sensor usages and high rate data acquisition involved in a monitoring system have considerable drawbacks of high system cost involved in purchasing hardware for data transfer, storage and processing. To reduce these shortages, this paper investigates compressive sensing (CS) techniques for the fault detection of rolling element bearings. Based on the frequency shift and envelope analysis, a CS scheme is developed for monitoring the bearing. The number of data transmitted and stored can be reduced by several thousands of times. The simulation and the experimental results demonstrate that the compressed vibration signals of rolling element bearings are effective to detect bearing faults at the total compressing ratio up to several thousand with the corresponding maximum compression ratio (CR) of CS process at nearly 100. In addition, several performance measures are applied to evaluate the reconstructed signals and show approximately the information about the noise level of the system.",
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Tang, X, Xu, Y, Gu, F & Wang, G 2017, Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing. in Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 23rd International Conference on Automation and Computing, Huddersfield, United Kingdom, 7/09/17. https://doi.org/10.23919/IConAC.2017.8082063

Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing. / Tang, Xiaoli; Xu, Yuandong; Gu, Fengshou; Wang, Guangbin.

Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 2017.

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

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T1 - Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing

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AU - Gu, Fengshou

AU - Wang, Guangbin

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Y1 - 2017/10/26

N2 - Rolling element bearings are the essential components of rotating machines, faults of which can cause serious failures or even major breakdowns of a machine. Fault diagnosis deliveries significant benefits to machines with rolling element bearings by finding the faults at early period and taking corrective actions to enhance safe and high performance operations. However, multiple sensor usages and high rate data acquisition involved in a monitoring system have considerable drawbacks of high system cost involved in purchasing hardware for data transfer, storage and processing. To reduce these shortages, this paper investigates compressive sensing (CS) techniques for the fault detection of rolling element bearings. Based on the frequency shift and envelope analysis, a CS scheme is developed for monitoring the bearing. The number of data transmitted and stored can be reduced by several thousands of times. The simulation and the experimental results demonstrate that the compressed vibration signals of rolling element bearings are effective to detect bearing faults at the total compressing ratio up to several thousand with the corresponding maximum compression ratio (CR) of CS process at nearly 100. In addition, several performance measures are applied to evaluate the reconstructed signals and show approximately the information about the noise level of the system.

AB - Rolling element bearings are the essential components of rotating machines, faults of which can cause serious failures or even major breakdowns of a machine. Fault diagnosis deliveries significant benefits to machines with rolling element bearings by finding the faults at early period and taking corrective actions to enhance safe and high performance operations. However, multiple sensor usages and high rate data acquisition involved in a monitoring system have considerable drawbacks of high system cost involved in purchasing hardware for data transfer, storage and processing. To reduce these shortages, this paper investigates compressive sensing (CS) techniques for the fault detection of rolling element bearings. Based on the frequency shift and envelope analysis, a CS scheme is developed for monitoring the bearing. The number of data transmitted and stored can be reduced by several thousands of times. The simulation and the experimental results demonstrate that the compressed vibration signals of rolling element bearings are effective to detect bearing faults at the total compressing ratio up to several thousand with the corresponding maximum compression ratio (CR) of CS process at nearly 100. In addition, several performance measures are applied to evaluate the reconstructed signals and show approximately the information about the noise level of the system.

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Tang X, Xu Y, Gu F, Wang G. Fault Detection of Rolling Element Bearings Using the Frequency Shift and Envelope based Compressive Sensing. In Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc. 2017 https://doi.org/10.23919/IConAC.2017.8082063