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,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-06, Vol.20 (12), p.3457
Main Authors: Ning, Jing, Fang, Mingkuan, Ran, Wei, Chen, Chunjun, Li, Yanping
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Decomposition
feature fusion
High speed rail
high-speed trains
hunting
Methods
Monitoring systems
multi-sensor fusion
Multisensor fusion
Noise
non-stationary
Pattern recognition
Principal components analysis
Sensors
Time series
Trains
Vibration
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20123457