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A Model To Predict the Residual Life of Aircraft Engines Based upon Oil Analysis Data

机译:基于机油分析数据的飞机发动机剩余寿命预测模型

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This paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness-of-fit test has been carried out.
机译:本文使用可利用的机油监控信息(例如使用光谱油分析程序(SOAP)获得的数据)报告一项研究,以预测飞机发动机的剩余寿命。机油监控信息与剩余寿命之间的关系是使用比例剩余的概念建立的,该关系指出,预测剩余寿命可能与机油分析程序测得的磨损增量成比例。假设存在磨损与剩余寿命之间的这种关系,我们根据迄今已测得的机油监测信息,为该项目的剩余寿命制定了递归预测模型。我们可以获取一组30架飞机发动机的寿命数据(由于预防性检修而进行了正确的检查)以及其监视的金属浓度信息的历史记录。金属浓度信息包括许多变量,例如Fe,Cu,Al等。并非所有这些都有用,并且其中一些可能是相关的。已采用主成分分析(PCA)来减少原始数据集的维数并产生一组新的不相关变量,我们将在预测模型中使用这些变量。讨论了与估计模型参数相关的过程。该模型适合飞机发动机的实际SOAP数据,并且已经进行了拟合优度测试。

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