Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains

Joint Approximate Diagonalization of Eigen-matrices (JADE) cannot deal with non-stationary data. Therefore, in this paper, a method called Non-stationary Kernel JADE (NKJADE) is proposed, which can extract non-stationary features and fuse multi-sensor features precisely and rapidly. In this method, the non-stationarity of the data is considered and the data from multi-sensor are used to fuse the features efficiently. The method is compared with EEMD-SVD-LTSA and EEMD-JADE using the bearing fault data of CWRU, and the validity of the method is verified. Considering that the vibration signals of high-speed trains are typically non-stationary, it is necessary to utilize a rapid feature fusion method to identify the evolutionary trends of hunting motions quickly before the phenomenon is fully manifested. In this paper, the proposed method is applied to identify the evolutionary trend of hunting motions quickly and accurately. Results verify that the accuracy of this method is much higher than that of the EEMD-JADE and EEMD-SVD-LTSA methods. This method can also be used to fuse multi-sensor features of non-stationary data rapidly.

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An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains

Measurement ◽

10.1016/j.measurement.2020.108746 ◽

2021 ◽

Vol 171 ◽

pp. 108746

Author(s):

Qingsong Zhang ◽

Jianming Ding ◽

Wentao Zhao

Keyword(s):

Wavelet Transform ◽

Fault Detection ◽

High Speed ◽

Determination Method ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Boundary Determination ◽

Empirical Wavelet Transform ◽

High Speed Trains

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A multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains

Journal of Vibration and Control ◽

10.1177/1077546318787945 ◽

2018 ◽

Vol 24 (17) ◽

pp. 3797-3808 ◽

Cited By ~ 2

Author(s):

Jing Ning ◽

Qi Liu ◽

Huajiang Ouyang ◽

Chunjun Chen ◽

Bing Zhang

Keyword(s):

Monitoring System ◽

Sensor Fusion ◽

Small Amplitude ◽

High Speed ◽

Railway Track ◽

Support Vector ◽

Monitoring Systems ◽

Operation Condition ◽

High Speed Trains ◽

Fusion Framework

Hunting monitoring is very important for high-speed trains to achieve safe operation. But all the monitoring systems are designed to detect hunting only after hunting has developed sufficiently. Under these circumstances, some damage may be caused to the railway track and train wheels. The work reported in this paper aims to solve the detection problem of small amplitude hunting before the lateral instability of high-speed trains occurs. But the information from a single sensor can only reflect the local operation state of a train. So, to improve the accuracy and robustness of the monitoring system, a multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains based on an improved Dempster–Shafer (DS) theory is proposed. The framework consists of a series of steps. Firstly, the method of combining empirical mode decomposition and sample entropy is used to extract features of each operation condition. Secondly, the posterior probability support vector machine is used to get the basic probability assignment. Finally, the DS theory improved by the authors is proposed to get a more accurate detection result. This framework developed by the authors is used on high-speed trains with success and experimental findings are provided. This multi-sensor fusion framework can also be used in other condition monitoring systems on high-speed trains, such as the gearbox monitoring system, from which nonstationary signals are acquired too.

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Arc Detection and Recognition in the Pantograph-Catenary System Based on Multi-Information Fusion

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120937964 ◽

2020 ◽

Vol 2674 (10) ◽

pp. 229-240

Author(s):

Shize Huang ◽

Wei Chen ◽

Bo Sun ◽

Ting Tao ◽

Lingyu Yang

Keyword(s):

Visible Light ◽

Information Fusion ◽

High Speed ◽

Evidence Theory ◽

Detection Methods ◽

Fusion Method ◽

Fusion Model ◽

Threshold Method ◽

High Speed Trains ◽

Catenary System

The pantograph-catenary system is critical to high-speed railways. Electric arcs in the pantograph-catenary system indicate possible damages to the whole railway system, and detecting them in time has been a critical task. In this paper, a fusion method for the pantograph-catenary arc detection based on multi-type videos is proposed. First, convolutional neural network (CNN) is employed to detect arcs in visible light images, and a threshold method is applied to identify arcs in infrared images. Second, the CNN-based environment perception model is established on visible light images. It obtains the dynamical adjustment of the reliability weights for different scenarios where trains usually work. Finally, the information fusion model based on evidence theory uses those weights and integrates the detection results on visible light images and infrared results. The experimental results demonstrate the fusion method can avoid misjudgments of the two individual detection methods in certain scenarios, and perform better than each of them. This approach can adapt to the complex environments of high-speed trains.

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Small-amplitude hunting diagnosis method for high-speed trains based on the bogie frame’s lateral–longitudinal–vertical data fusion, independent mode function reconstruction and linear local tangent space alignment

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409718825412 ◽

2019 ◽

Vol 233 (10) ◽

pp. 1050-1067 ◽

Cited By ~ 8

Author(s):

Yunguang Ye ◽

Jing Ning

Keyword(s):

Data Fusion ◽

Small Amplitude ◽

High Speed ◽

Ride Comfort ◽

Monitoring Methods ◽

Vertical Data ◽

High Speed Trains ◽

Diagnosis Method ◽

Local Tangent ◽

Independent Mode

Hunting stability is an important factor that influences the running safety of high-speed trains. Most of the existing hunting monitoring methods monitor only the standard hunting. The small-amplitude hunting, however, not only affects ride comfort but also aggravates the wheel–rail wear. Therefore, to extend the service life of wheels and rails and to improve the ride comfort, it is extremely important to monitor the small-amplitude hunting. Hunting motion is a coupled movement of lateral and yaw displacements of the wheelset. When the bogie is in an unstable state, instability will occur not only in the lateral side but also in the longitudinal and vertical sides of the bogie. To improve the robustness of the small-amplitude hunting monitoring methods, this study proposes an idea of the bogie frame’s lateral–longitudinal–vertical data fusion. In addition, the small-amplitude hunting signals have strong nonlinear characteristics, and their frequency and amplitude are unstable. Using only the amplitude or frequency to detect the small-amplitude hunting has obvious shortcomings. Therefore, a new feature extraction method based on the independent mode function reconstruction and linear local tangent space alignment (IMFR-LLTSA) is proposed. This method has been tested with three simulated signals. Finally, a method of combining the bogie frame’s lateral–longitudinal–vertical data fusion and IMFR-LLTSA is used to identify the small-amplitude hunting of high-speed trains. This method has been validated using the data of the CRH380a high-speed train running on the Shanghai–Hangzhou line, monitored by the authors’ research group. The results show that this method is superior to the single lateral diagnosis method.

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Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation

Shock and Vibration ◽

10.1155/2019/7198693 ◽

2019 ◽

Vol 2019 ◽

pp. 1-26

Author(s):

Jianming Ding ◽

Zhaoheng Zhang ◽

Yanli Yin

Keyword(s):

Fault Detection ◽

High Speed ◽

Real Life ◽

High Speed Train ◽

Impulse Responses ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Convolution Model ◽

High Speed Trains ◽

Bench Testing

Wheelset bearings are crucial mechanical components of high-speed trains. Wheelset-bearing fault detection is of great significance to ensure the safety of high-speed train service. Convolution sparse representations (CSRs) provide an excellent framework for extracting impulse responses induced by bearing faults. However, the performance of CSR on extracting impulse responses is fairly sensitive to inappropriate selection of method-related parameters, and a convolution model for representing the impulse responses has not been discussed. In view of these two unsolved problems, a convolutional representation model of the impulse response series is developed. A novel fault detection method, named adaptive CSR (ACSR), is then proposed based on combinations of CSR and methods for estimating three parameters related to CSR. Finally, the effectiveness of the proposed ACSR method is validated via simulation, bench testing, and a real-life running test employing a high-speed train.

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