Engine Condition Monitoring Based on Grey AR Combination Model

Qiang Wang, Sheng Hui Dai
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引用次数: 1

Abstract

Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.
基于灰色AR组合模型的发动机状态监测
针对磨损状态监测中存在的问题,提出了灰色理论和自回归组合预测模型,并建立了组合预测模型。通过灰色理论可以反映磨损颗粒含量变化的大致趋势,通过自回归模型可以反映变化的细节。通过对一组Ferro图形数据的检验和比较,结果表明该组合模型具有较好的预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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