Dynamic Predictive Maintenance Model Based on Data-Driven Machinery Prognostics Approach

W.Z. Liao; Y. Wang

doi:10.4028/www.scientific.net/AMM.143-144.901

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Dynamic Predictive Maintenance Model Based on Data-Driven Machinery Prognostics Approach

Abstract:

As an increasing number of manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, machinery prognostics which enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machine's condition and degradation estimated by machinery prognostics approach can be used to support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to assess machine's health condition and predict machine degradation. With this prognostics information, a predictive maintenance model is constructed to decide machine's maintenance threshold and predictive maintenance cycles number. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.