科研之友
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摘要
As an increasing number of manufacturers are beginning to realize the importance of maintaining throughput and adopting new maintenance technologies to enable systems to achieve near-zero downtime, machinery prognostics which enables this paradigm shift from the traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. In machinery prognostics, machine's condition and degradation are two important issues. This paper develops a novel data-driven machine performance assessment and prediction approach based on statistical pattern recognition and auto-regressive and moving average model to assess machine's health condition and predict machine's remaining useful life, which supports prognostics. Through a case study, this developed data-driven machine performance assessment and prediction approach is verified. The computational results show that this approach is efficient and practical.
