Abstract
The localized faults of bearings can be diagnosed by extracting approximately periodic impulses from vibration signals. However, this feature may be deeply submerged in the high-level noise. In this article, a novel collaborative double sparse period-group lasso (CDSPGL) algorithm is proposed. The algorithm is based on two main priors of the fault bearing signal. The first is provided by the resonance frequency, and the second is provided by the fault characteristic frequency. Moreover, a novel collaborative period estimation strategy is developed to interact with the two priors according to the structural relationship between the two group-sparse models. Meanwhile, selection rules of regularization parameters are discussed in detail. Finally, the superiority of CDSPGL is verified through numerical simulation and diagnostic application.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Instrumentation and Measurement
Disclaimer: 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.