A prognostic algorithm for machine performance assessment and its application
A prognostic algorithm for machine performance assessment and its application
复制标题
DOI：
10.1080/09537280412331309208
复制
发表时间：
2004-12
影响因子：
8.3
通讯作者：
Jihong Yan;M. Koç;Jay Lee
中科院分区：
文献类型：
--
作者：
Jihong Yan;M. Koç;Jay Lee
本文探讨了一种评估资产性能和预测剩余使用寿命的方法，这将导致主动维护过程，以最大限度地减少各种行业的机械和生产停机时间，从而提高运营和制造效率。首先，利用最大似然技术进行逻辑回归分析，建立了性能模型。详细讨论了有或没有足够历史数据的两种应用情况。然后，通过将在线数据的特征输入到逻辑模型中来评估实时性能。最后，利用基于机器性能历史的ARMA模型估计剩余寿命；退化预测也会动态升级。将当前机器运行状况和剩余使用寿命等结果输出到维护决策模块，以便在机器出现故障之前确定适当的维护窗口。最后给出了该方法在电梯门运动系统中的应用。
This paper explores a method to assess assets performance and predict the remaining useful life, which would lead to proactive maintenance processes to minimize downtime of machinery and production in various industries, thus increasing efficiency of operations and manufacturing. At first, a performance model is established by taking advantage of logistic regression analysis with maximum-likelihood technique. Two kinds of application situations, with or without enough historical data, are discussed in detail. Then, real-time performance is evaluated by inputting features of online data to the logistic model. Finally, the remaining life is estimated using an ARMA model based on machine performance history; degradation predictions are also upgraded dynamically. The results such as current machine running condition and the remaining useful life, are output to the maintenance decision module to determine a window of appropriate maintenance before the machine fails. An application of the method on an elevator door motion system is demonstrated.