Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

Abstract

The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

“…E(h(x)) is the average path length over t iTrees, which is obtained by calculating the average value of x at the height of each iTree. After obtaining the average path length of each test data, the abnormal score is calculated using Equation (8).…”

“…However, before leaving the factory, a high-speed EMU is equipped with many other sensors, such as sensors to monitor the axle box A fault early warning is a part of condition monitoring and different methods are used for the diagnosis of rotary machines, such as bearing, gears, and motors. According to data sources, they can be broadly classified as vibration-based data [1][2][3][4][5][6], acoustic-based data [7,8], temperature-based data [9,10], and fusion-based data [11][12][13][14][15][16][17].…”

Doppler distortion elimination using short-time sparse singular value decomposition strategy for wayside acoustic source fault diagnosis

“…It could be easily verified that when far-field measurement is taken and M < 0.2, A 2 is far less than A 1 , which implies that the second part in equation (4) can be disregarded for simplicity. 32 When parameters ϕ and v are set to 0, the targeted acoustic source is static. Hence, we can obtain the corresponding acoustic pressure as follows …”

Wayside acoustic fault diagnosis of train wheel bearing based on Doppler effect correction and fault-relevant information enhancement

“…6 However, given the relatively high moving speed between the microphone and the train, the Doppler effect will make the WAS a serious distortion. 7 Consequently, the signals undergo amplitude modulation and frequency band expansion. The collected WAS are also submerged in heavy noises, such as rolling noise, 8 mechanical structural borne noise radiated by car body vibration, 9 and aerodynamic noise.…”