涡扇发动机
主成分分析
概率逻辑
计算机科学
维数之咒
人工神经网络
数据建模
概率神经网络
人工智能
数据挖掘
统计模型
模式识别(心理学)
工程类
数据库
汽车工程
时滞神经网络
作者
Hanghang Sun,Yongdong Wang,Yandong Hou
标识
DOI:10.1109/safeprocess52771.2021.9693652
摘要
As the core of the aircraft, the prediction of the remaining useful life(RUL) of the engine is of great significance to the stable operation of the aircraft. A remaining useful life prediction model based on a combination of Probabilistic Principal Components Analysis(PPCA) and Gated Recurrent Unit(GRU) is proposed for complex system monitoring data with large data volume and high dimensionality. The model uses PPCA to reduce the dimensionality of the original data, combines the Gaussian model with the Principal Components Analysis (PCA) and uses the Expectation Maximum (EM) algorithm to solve the best probability model in order to maximize the retention of useful information from the original data. The processed data is fed into the GRU model to mine the time series information, and finally, the model is further modified by the difference between the predicted and actual values. Simulation experiments using the C-MAPSS turbofan engine dataset and compared with the results of related studies such as support vector machines and Long-Short Term Memory(LSTM) neural network. The prediction results prove effectiveness of the method and provide support for predicting the remaining useful life.
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