Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

Application of OpenFOAM in Numerical Simulations of High-Speed Trains Aerodynamics

2021 ◽  
Author(s):  
Panpan Lu ◽  
Bo Yin ◽  
Guowei Yang ◽  
Zhanling Ji
Keyword(s):  
Turbulent Flow ◽  
Mesh Generation ◽  
High Speed ◽  
Aerodynamic Performance ◽  
Critical Conditions ◽  
High Speed Train ◽  
Operation Conditions ◽  
Mesh Method ◽  
High Speed Trains ◽  
Inverse Distance

Abstract The present study uses openFOAM package to simulate and investigate the aerodynamic performance and characteristics of high-speed trains (velocity beyond 250 km/h) under typical and critical conditions, which include flow passing a high-speed train, and two trains meeting towards each other at the same velocity in the open air. In terms of different operation conditions, separate openFOAM solvers are adopted. For flow passing a high-speed train at a constant velocity, a steady solver for incompressible, viscous and turbulent flow is employed on a fixed mesh and the results are compared with commercial software Star CCM+. For trains meeting towards each other, overset mesh method is used in which inverse distance interpolation is taken to couple background and inner overset mesh. The built-in mesh generation tool SnappyHexMesh is utilized to generate background and inner overset mesh. In all simulations, k-ω SST two equations RANS model is used to simulate the turbulent flow.

scholarly journals Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 2050 ◽  
Author(s):  
JaeYoung Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Effective Form ◽  
Fault Features ◽  
Rolling Elements ◽  
Bearing Characteristic

Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure.

scholarly journals Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4930 ◽  
Author(s):  
Honglin Luo ◽  
Lin Bo ◽  
Chang Peng ◽  
Dongming Hou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
High Speed ◽  
High Reliability ◽  
Operating Conditions ◽  
High Speed Train ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

scholarly journals State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1017 ◽  
Author(s):  
Chao Cheng ◽  
Weijun Wang ◽  
Hao Luo ◽  
Bangcheng Zhang ◽  
Guoli Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Information Acquisition ◽  
High Speed ◽  
Bilinear System ◽  
Degradation Process ◽  
High Speed Train ◽  
Operation Performance ◽  
High Speed Trains ◽  
Critical Components ◽  
Open Datasets

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

An Adaptive Multi-Sensor Fault Diagnosis Method for High-Speed Train Traction Converters

2020 ◽  
pp. 1-1
Author(s):  
Honghui Dong ◽  
Fuzhao Chen ◽  
zhipeng wang ◽  
Limin Jia ◽  
Yong Qin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Sensor Fault ◽  
Diagnosis Method
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