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Open AccessArticle

Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
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Information 2020, 11(6), 298; https://doi.org/10.3390/info11060298
Received: 3 May 2020 / Revised: 21 May 2020 / Accepted: 26 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Signal Processing and Machine Learning)
The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%. View Full-Text
Keywords: wavelet packet; cross-correlation analysis; feature extraction; lateral damper wavelet packet; cross-correlation analysis; feature extraction; lateral damper
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Zhao, Z.; Wu, B.; Zhou, T. Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper. Information 2020, 11, 298.

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