Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching. (1st January 2016)

• Record Type: Journal Article
• Authors: Wang, Chao; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Fang; Kong, Fanrang
• Is Part Of: Applied acoustics. Volume 101(2016)
• Page Start: 141
• Page End: 155
• Publication Date: 2016-01-01
• Subjects: Train bearing -- Wayside fault diagnosis -- Doppler Effect -- Dopplerlet transform -- Doppler transient matching; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2015.08.014
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: A double-searching algorithm is developed to improve effectiveness of ADBD system. This algorithm could extract useful information in a heavy noise background. There is no pre-measurement carried out in the algorithm. Proposed algorithm has superior performance than the existing method. Proposed method has been verified by simulation and experimental cases. Abstract: The diagnosis of train bearing defects plays a significant role in maintaining the safety of railway transport. However, the phenomenon of Doppler Effect in the acoustic signal recorded by the wayside Acoustic Defective Bearing Detector (ADBD) system leads to the difficulty for fault diagnosis of train bearings with a high moving speed. This paper proposes a double-searching solution based on improved Dopplerlet transform and Doppler transient matching to overcome the difficulty in wayside acoustic bearing diagnosis. In the solution, the first searching procedure is to extract necessary parameters of Doppler Effect under the situation with very low signal-to-noise ratio (SNR) based on an improved Dopplerlet transform. Using the obtained parameters, the Doppler Effect can be embedded into the constructed periodic Laplace wavelet transient models. Subsequently, the second searching procedure is conducted to search fault impact period of the defective bearing through an operation, called Doppler transient matching, which is to calculate the correlation coefficient between the Doppler transient model and the filtered raw signal with the Doppler Effect. The proposed double-searching algorithm can adapt to the real Doppler Effect situation and extract the exact fault impact period from the Doppler distorted signal, and thus shows powerful capability to analyze wayside acoustic signals from train bearings. The proposed wayside acoustic diagnostic scheme is verified by means of a simulated Doppler distorted signal with a very low SNR (−20 dB) and the experiments conducted on train bearings. The results indicate that the proposed algorithm is effective and has obvious advantages for ADBD system.