Abstract
Early detecting and diagnosing bearing defects during operation aid in preventing abnormal fault progres-sion and decrease productivity loss. Vibration signal analy-sis is one of the most widely applied methods for bearing problem diagnosis, for its effectiveness and easy manipulation. Time-frequency analysis has received considerable interest in the field of bearing fault detection over the past few decades. A key element of this procedure is extracting informative features from the TFRs. In this report, we have developed a method based on cloud point dispersion parameter. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). This approach is validated on an academic case and then tested on real data taken from the PRONOSTIA experimental platform. DOI: http://dx.doi.org/10.5755/j01.mech.20.6.6738
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.
