Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement

Dai

et al. 2017

Shock and Vibration

Bearing is a key part of rotary machines, and its working condition is critical in normal operation of rotary machines. Vibrational signals are usually analyzed to monitor the status of bearing. However, information on the status of bearing is always buried in heavy background noise; that is, status information of bearing is weaker than the background noise. Extracting the status features of bearing from signals buried in noise is difficult. Given this, a step-varying vibrational resonance (SVVR) method based on Duffing oscillator nonlinear system is proposed to enhance the weak status feature of bearing by tuning different parameters. Extraction ability of SVVR was verified by analyzing simulation signal and practical bearing signal. Experimental results show that SVVR is more effective in extracting weak characteristic information than other methods, including multiscale noise tuning stochastic resonance (SR), Woods-Saxon potential-based SR, and joint Woods-Saxon and Gaussian potential-based SR. Two evaluation indices are investigated to qualitatively and quantitatively assess the fault detection capability of the SVVR method. The results show that the SVVR can effectively identify the weak status information of bearing.

Section: Discussion Some Discussion Are Listed As Followsmentioning

confidence: 99%

“…The SNRs from each subfigure imply that the SVVR method is more effective in extracting the weak feature component than the MSTSR method. A higher SNR indicates a more accurate detection performance for fault signals [31][32][33][34].…”

Section: Simulation Signal Analysismentioning

confidence: 99%

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Dai

et al. 2017

Shock and Vibration

“…For the speed increase, it is an important mission to guarantee the safety, the stability and uninterrupted operation of trains for passenger and freight transportation. There are hundreds of rolling bearings in a train with a significant relation for the train running [2]. As reported, bearing failure is the most common type of train faults [3], [4].…”

Section: Introductionmentioning

confidence: 98%

“…But if we monitor the train bearing with the acoustic signal acquired from wayside, it is obvious that the measurement suffers from the drawback of signal attenuation and difficulty of signal processing, interpreting and classifying, as well as the difficulty of detecting inner race defects of a bearing [7]. Besides, as we discussed in the abstract, the acoustic signal we acquire from wayside is corrupted by the Doppler Effect and surrounding heavy noise [2]. So we need a lot of ways or algorithm to solve these challenges.…”

Section: Introductionmentioning

confidence: 99%

A Data-Collection and Fault Diagnosis System of Train Bearing Based on the C# and Multi-Wireless-Terminal

Zhang¹,

Lu²,

Zhang³

et al. 2015

JSW

Self Cite

With the development of modern railway transportation, the diagnosis of train bearing defects plays a significant role to maintain the safety of it and the higher speed, the more reliable detection we need. Among various defect detection techniques, acoustic diagnosis is widely used for detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. But as we know, the acoustic signal we acquire from wayside is corrupted by the Doppler Effect and surrounding heavy noise including the noise of train body vibration, the noise of wheels, aerodynamic noise, etc. Besides, as the signal acts as a mixture of many bearings of the train, if we want to get the feature of one of them, we also need to have method to separate the signal. These are all quite difficult works. This paper will show us a new way to identify the conditions of bearings from a running machine. In this paper, we mainly describe the design of a software framework based on C# and the whole data transportation system. Ubiquitous computing technologies offer excitingly new possibilities for monitoring and analyzing of the train bearing conditions in real time. In our system, we put forward a whole serial port communication protocol which insures the data's accuracy during the wireless transmission process. We also get a thorough logic between the PC and wireless-terminals. In the software, we make it possible for engineers to monitor the bearings' condition in real time, check the parameters calculated from original data to identify the fault and set the communication parameters. Above all, the signals we acquire here are the vibration signal collected from the train bearings directly. It will make up most of the shortcomings compared to the acoustic diagnosis.

“…1,2 Train bearing fault diagnosis can be performed through several methods, such as oil monitoring, 3 hot-box detection, 4 vibration signal analysis, 5 and acoustic signal analysis. 6 Wayside acoustic signal (WAS) diagnosis has a clear advantage over other methods because the acoustic signal acquisition device does not come into contact with the wheel bearings. As a train passes over a track, the WAS of wheel bearings can be obtained with a wayside microphone mounted next to the track.…”

Section: Introductionmentioning

confidence: 99%

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

Qian

Advances in Mechanical Engineering

et al. 2017

Health monitoring of train bearing is crucial to railway transport safety. More and more attention has elicited by the wayside acoustic monitoring technique in recent years than other defect detection techniques. However, wayside acoustic signal contains serious Doppler distortion and heavy background noise because of the high speed of trains. Thus, extracting fault-relevant information is difficult. A novel method for Doppler effect correction is proposed in this study by incorporating the traditional time-domain interpolation resampling with a novel kinematic parameters estimation method. In this kinematic parameters estimation method, an iterative algorithm based on least squares theory is proposed to improve the parameters estimation accuracy. After the Doppler effect correction, the ensemble empirical mode decomposition is employed to further enhance the fault-relevant information. The proposed iteration algorithm can improve the accuracy of kinematic parameters estimation significantly; thus the Doppler distortion can be corrected more accurately. The proposed ensemble empirical mode decomposition can further enhance the fault-relevant information and so that the accuracy and reliability of the diagnosis decision can be improved. The performance of this method has been verified in experimental and simulated cases.