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

• Published in: International Journal of Rotating Machinery 2016-01, Vol.2016 (2016), p.247-253
• Main Authors: Cui, Yongxiang, Lei, Wenping, Han, Jie, Chen, Lei, Guan, Zhenhong
• Format: Article
• Language: English
• Subjects: Algorithms; Data acquisition systems; Diagnosis; Direct reduction; Failure; Fault diagnosis; Faults; Mathematical models; Methodology; Predictive maintenance
• ISSN: 1023-621X; 1542-3034
• DOI: 10.1155/2016/5980802

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 algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was 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.