A priori indicator identification to support predictive maintenance: application to machine tool
Predictive maintenance requires the identification of the parameters to be monitored and sensors to be implemented on a system. In industrial companies, usually such goal is tackled by implementing sensor and after see if one can extract some indicators related to degradation. The lack of methodology makes the benefits of predictive maintenance to be lower than expected. Indeed, its implementation is done by “the rule of the thumb” using some metrics “a posteriori” in order to show the relevancy of the instrumentation. Hence, a structured approach is required in order to define “a priori” the most suitable indicators to be relevant for degradation monitoring and related instrumentation to be implemented. Thus, the paper presents a methodology based on a coupled approach of FMECA and Hazard Operability analysis (HAZOP) which aim is to contribute to the deployment of predictive maintenance strategies by clearly identify pertinent indicator. This approach is based on the formalization of concepts of knowledge which permit to constitute the first pillars of predictive maintenance approach. The formalization step leads to promote meta-model and reference model of knowledge. The feasibility and the interests of such approach are shown on the case of machine tool GROB G520 located in RENAULT Cléon Factory. It consists in particularizing the reference model proposed to identify automatically and in a more efficient the right indicators/parameters on which the predictive maintenance of this machine tool should be based.
How to Cite
predictive maintenance; monitoring; machine tool
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.