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Key Technologies for Health Management and Applications of Aero- Engine Based on Principal Component Analysis

机译:基于主成分分析的空气发动机健康管理与应用关键技术

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Aero-Engine health management generally involves a series of activities over the period from the aerospace breaking down until it returning to normal, including signal processing, monitoring, health assessment, decision supporting, human-computer interaction, and so on. As one of the key technology of Aero-Engine health management, fault diagnosis plays a very important role on the safe operation of Aero-Engine. Currently, for effective challenging Aero-Engine health management, a fault diagnosis of Aero-Engine based on Principal Component Analysis (PCA) s proposed. Firstly, based on a variety of significant parameters of the collected information, principal component analysis model is established. Secondly, the fault diagnosis of engine operating conditions is realized by comparing the 2 T statistic and Squared Prediction Error (SPE) statistic as an engine running in good condition threshold limits. Finally, through the variable's cumulative contributions diagram with the behavior of SPE overrun, the fault variables are effectively worked out. Experimental results show that the proposed PCA method can efficiently come true Aero- Engine health management o and has some engineering applications values.
机译:航空发动机健康管理通常涉及一系列活动,从航空空间分解,直到它恢复正常,包括信号处理,监测,健康评估,决策支持,人机互动等。作为航空发动机健康管理的关键技术之一,故障诊断在航空发动机的安全运行中起着非常重要的作用。目前,为了有效具有挑战性的航空发动机健康管理,基于主成分分析(PCA)S提出的航空发动机故障诊断。首先,基于所收集信息的各种重要参数,建立了主成分分析模型。其次,通过将2 T统计和平方预测误差(SPE)统计作为在良好状态阈值限制中运行的发动机来实现发动机操作条件的故障诊断。最后,通过变量的累积贡献图与SPE超支的行为,故障变量有效地解决了。实验结果表明,所提出的PCA方法可以有效地实现气动发动机健康管理o,并具有一些工程应用价值。

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