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논문 기본 정보

자료유형
학술저널
저자정보
Xiao-Zhou Li (Hong Kong Branch of Chinese National Rail Transit) Yi-Qing Ni (The Hong Kong Polytechnic University)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.21 No.5
발행연도
2018.1
수록면
687 - 694 (8page)

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초록· 키워드

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The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

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참고문헌 (25)

참고문헌 신청
Asplund, M. / 2014 / Condition monitoring and e-maintenance solution of railway wheels / J. Qual. Maint. Eng. 20(3):216~232 Crossref Baeza, L. / 2006 / Railway train-track dynamics for wheelflats with improved contact models / Nonlinear Dynam. 45(3-4):385~397 Crossref Barke, D. / 2005 / Structural health monitoring in the railway industry: a review / Struct. Health Monit. 4(1):81~93 Crossref Bian, J. / 2013 / A dynamic wheel-rail impact analysis of railway track under wheel flat by finite element analysis / Vehicle. Syst. Dyn. 51(6):784~797 Crossref Ding, J. / 2014 / Time-frequency analysis of wheel-rail shock in the presence of wheel flat / J. Traffic Transp. Eng. (English Edition) 1(6):457~466 Crossref

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