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http://hdl.handle.net/10397/106387
Title: | Fault detection of wheel in wheel/rail system using kurtosis beamforming method | Authors: | Chen, L Choy, YS Wang, TG Chiang, YK |
Issue Date: | Mar-2020 | Source: | Structural health monitoring, Mar. 2020, v. 19, no. 2, p. 495-509 | Abstract: | Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains. | Keywords: | Array signal processing Impulsive signal Kurtosis Time-domain beamforming Wheel–rail contact |
Publisher: | Sage Publications Ltd. | Journal: | Structural health monitoring | ISSN: | 1475-9217 | EISSN: | 1741-3168 | DOI: | 10.1177/1475921719855444 | Rights: | This is the accepted version of the publication Chen L, Choy YS, Wang TG, Chiang YK. Fault detection of wheel in wheel/rail system using kurtosis beamforming method. Structural Health Monitoring. 2020;19(2):495-509. Copyright © 2019 The Author(s). DOI: 10.1177/1475921719855444. |
Appears in Collections: | Journal/Magazine Article |
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Chen_Fault_Detection_Wheel.pdf | Pre-Published version | 1.9 MB | Adobe PDF | View/Open |
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