Abstract
For precise and reliable fault detection it is essential to consider simultaneously the changes in several diagnostic indices that are extracted from the on-line vibration signal. Existing machine condition monitoring systems consider each diagnostic index separately. Development of an automated diagnostic procedure that considers simultaneously several diagnostic indices is the objective of the present paper. The multivariable trend analysis of on-line vibration signals is deployed as the basis for this procedure. An efficient self-organizing neural network algorithm that is highly suitable to this diagnostic procedure is developed and deployed. Applications to both a bearing system as well as a gearbox system are fully developed and demonstrated.
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 callMore From: Journal of Dynamic Systems, Measurement, and Control
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.
