A two-level adaptive chirp mode decomposition method for the railway wheel flat detection under variable-speed conditions. (28th April 2021)

• Record Type: Journal Article
• Authors: Chen, Shiqian; Wang, Kaiyun; Chang, Chao; Xie, Bo; Zhai, Wanming
• Is Part Of: Journal of sound and vibration. Volume 498(2021)
• Publication Date: 2021-04-28
• Subjects: Wheel flat -- Fault diagnosis -- Variable speed -- Adaptive mode decomposition -- Time-frequency analysis; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2021.115963
• Languages: English
• ISSNs: 0022-460X

Abstract

Highlights: A two-level ACMD is developed to detect wheel flats under variable-speed conditions. L1-ACMD can separate different fault signal modes under severe interferences. L2-ACMD can achieve high-resolution time-varying characteristic frequency extraction. The effectiveness of the method is validated by dynamics simulation and experiment. Abstract: Wheel flat, as a common defect of railway vehicles, can cause large impact forces on both the vehicle and infrastructure components and thus seriously hinder the vehicle running stability and safety. Considering the complex track irregularities and variable-speed conditions, the vehicle vibration responses often contain strong interference signal components and exhibit time-varying frequency contents, which poses severe challenges to wheel flat detection. In this paper, a vehicle-track coupled model considering wheel flats under variable-speed conditions is employed to calculate and analyze the vehicle vibration accelerations at first. Then, according to the vibration characteristics, a two-level adaptive chirp mode decomposition (ACMD) approach is developed for the wheel flat detection. Specifically, in Level 1 of the approach, the ACMD is integrated with a Gini index-based mode selection and regrouping scheme to separate different fault signal modes under strong interferences. Based on the separated signal modes, the high-resolution ACMD-based time-frequency analysis method is applied to accurately extracting the time-varying fault characteristic frequencies and thus achieving the fault detection in Level 2. Both the dynamics simulation and experiment results indicate that the proposed approach can accurately detect wheel flats of small sizes under strong interferences for a variable-speed railway vehicle.