Abstract
Predictive maintenance can save a lot of efforts in modern industry and condition monitoring is attracting a lot of attention accordingly. New algorithms for fault detection appear frequently in the technical literature, however an objective, quantitative and widely accepted approach to performance comparison is still lacking. In this paper, we propose a new method leading to a fair and reproducible performance assessment. The proposed solution is based on vibrational analysis and consists of searching and detecting the theoretical cyclic frequencies that appear as a specific “signature” of a fault. Each algorithm for condition monitoring relies on a metric, then the main idea is to quantitatively characterize the peaks of the metric emerging from the machine noise. We think that the wide adoption of the proposed approach could significantly foster the research in the fields of condition monitoring and predictive maintenance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.