Abstract
In order to accomplish the feature extraction from a mixed fault signal of bearings, this paper proposes a feature extraction method based on the improved Fast-ICA algorithm and the wavelet packet energy spectrum. The conventional fast-ICA algorithm can only separate the mixed signals, while the convergence speed is relatively slow and the convergence effect is not sufficient. The method of the third-order Newton iteration is adopted in this paper to improve the Fast-ICA algorithm. Moreover, the improved Fast-ICA algorithm is confirmed to have a faster convergence speed and higher precision than the conventional Fast-ICA algorithm. The improved Fast-ICA algorithm is applied to separate the acoustic emission signal in which two kinds of fault components are comprised. The wavelet packet energy spectrum is used to extract the feature information in the separated samples. In addition, the fault diagnosis is performed based on the SVM algorithm. It is confirmed that the slight damage and fracture of a bearing can accurately be recognized. The results show that the improved FAST-ICA and wavelet packet energy method in feature extraction is sufficiently effective.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
