Abstract
Most previously developed algorithms used for machinery diagnostics and condition monitoring are based on the assumption of signal stationarity. Little attention has been paid to the time-dependent details of vibration signals. In this paper, non-stationary modelling of vibration signals is proposed. An auto-regressive model and the corresponding processes of feature extraction and condition monitoring are investigated in detail. Orthogonal transformation is then introduced for both feature compression and the establishment of a statistical model of the template representing the normal condition. Lastly, the monitoring measure is determined by using similarity analysis of pattern recognition theory. An example for the vibration monitoring of a hydraulic piston pump shows how the proposed method can be used.
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.
