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Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction


Affiliations
1 Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
 

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithmwere analyzed.At last, the fault predictionmethod based on full-vector spectrumwas proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.
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  • Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

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Authors

Lei Chen
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
Jie Han
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
Wenping Lei
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
Yongxiang Cui
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
Zhenhong Guan
Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China

Abstract


Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithmwere analyzed.At last, the fault predictionmethod based on full-vector spectrumwas proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.