Title :
Rotating machine fault diagnosis based on denoising source separation
Author :
Yuansheng Wang ; Xingmin Ren ; Guofang Nan ; Yongfeng Yang ; Wangqun Deng
Author_Institution :
Inst. of Vibration Eng., Northwestern Polytech. Univ., Xi´´an, China
Abstract :
The application of denoising source separation (DSS) technology to the mechanical vibration signal processing provides a new technique of mechanical fault diagnosis. The DSS theory is investigated in this paper. The analog signals of the rotating machine are separated and the performance index and correlation coefficient of DSS are better than those of the joint approximate diagonalization of eigen-matrices (JADE). Applying the DSS method to rotating machine fault diagnosis, the measured fault signals are analyzed and the results are found to be in agreement with practice. The results show that the DSS method is efficient in analyzing the fault diagnosis of rotating machine.
Keywords :
approximation theory; condition monitoring; eigenvalues and eigenfunctions; fault diagnosis; mechanical engineering computing; signal denoising; source separation; turbomachinery; vibrations; DSS correlation coefficient; DSS performance index; DSS technology; JADE; analog signals; denoising source separation technology; joint approximate diagonalization of eigenmatrices; mechanical fault diagnosis; mechanical vibration signal processing; rotating machines; Covariance matrix; Decision support systems; Fault diagnosis; Noise reduction; Rotating machines; Source separation; Vibrations;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463348
