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1. 发明授权

US08600917B1 Coupling time evolution model with empirical regression model to estimate mechanical wear 有权
标题翻译：耦合时间演化模型与经验回归模型估计机械磨损
公开(公告)号：US08600917B1
公开(公告)日：2013-12-03
申请号：US13088683
申请日：2011-04-18
申请人： James Schimert , Arthur Ray Wineland
发明人： James Schimert , Arthur Ray Wineland
IPC分类号： G06N5/00
CPC分类号： G06N99/005
摘要： Mechanical systems wear or change over time. Data collected over a system's life can be input to statistical learning models to predict this wear/change. Previous work by the inventors trained a flexible empirical regression model at a fixed point of wear, and then applied it independently at time points over the life of an engine to predict wear. The embodiment disclosed herein relates those wear predictions over time using a time evolution model. The time evolution model is sequentially updated with new data, and effectively tunes the empirical model for each engine. The combined model predicts wear with dramatically reduced variability. The benefit of reduced variability is that engine wear is more evident, and it is possible to detect operational anomalies more quickly. In addition to tracking wear, the model is also used as the basis for a Bayesian approach to monitor for sudden changes and reject outliers, and adapt the model after these events.
摘要翻译：机械系统磨损或随时间变化。通过系统生命收集的数据可以输入统计学习模型来预测这种磨损/变化。发明者的以前的工作在固定的磨损点上训练了灵活的经验回归模型，然后在发动机寿命期间的时间点独立地应用它来预测磨损。本文所公开的实施例使用时间演化模型来描述随时间的磨损预测。时间演化模型使用新数据顺序更新，并有效地调整每个引擎的经验模型。组合模型预测磨损显着降低的变异性。减少变异性的好处是发动机磨损更加明显，并且可以更快地检测操作异常。除了跟踪磨损，该模型还被用作贝叶斯方法监测突发变化和拒绝异常值的基础，并在这些事件之后适应模型。

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