Data-driven Machinery Prognostics Approach using in a Predictive Maintenance Model

Nowadays, more and more manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, so machinery prognostics that enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machinery prognostics which could estimate machine condition and degradation strongly support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to predict machine's health condition and describe machine degradation. Based on machine's prognostics information, a predictive maintenance model is well constructed to decide machine's optimal maintenance threshold and maintenance cycles. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.