预言
连接词(语言学)
藤蔓copula
数据挖掘
非线性系统
降级(电信)
计算机科学
过程(计算)
维纳过程
工程类
可靠性工程
人工智能
机器学习
电子工程
多元统计
数学
统计
物理
量子力学
计量经济学
操作系统
出处
期刊:Heliyon
[Elsevier]
日期:2023-06-01
卷期号:9 (6): e17118-e17118
标识
DOI:10.1016/j.heliyon.2023.e17118
摘要
Aeroengine is a highly complex and precise mechanical system. As the heart of an aircraft, it has a crucial impact on the overall life of the aircraft. Engine degradation process is caused by multiple factors, so multi-sensor signals are used for condition monitoring and prognostics of engine performance degradation. Compared with the single sensor signal, the multi-sensor signals can more comprehensively contain the degradation information of the engine and achieve higher prediction accuracy of the remaining useful life (RUL). Therefore, a new method for predicting the RUL of an engine based on R-Vine Copula under multi-sensor data is proposed. Firstly, aiming at the phenomenon that the engine performance parameters change over time, and the performance degradation presents nonlinear characteristics, the nonlinear Wiener process is used to model the degradation process of a single degradation signal. Secondly, the model parameters are estimated in the offline stage to integrate the historical data to obtain the offline parameters of the model. In the online stage, when the real-time data is obtained, the Bayesian method is used to update the model parameters. Then, the R-Vine Copula is used to model the correlation between multi-sensor degradation signals to realize online prediction of the remaining useful life of the engine. Finally, the C-MAPSS dataset is selected to verify the effectiveness of the proposed method. The experimental results show that the proposed method can effectively improve prediction accuracy.
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