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A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system

Neurocomputing, v.275, 2018년, pp.2111 - 2125  

Ren, Hao (School of Automation, Chongqing University) ,  Chai, Yi (School of Automation, Chongqing University) ,  Qu, Jianfeng (School of Automation, Chongqing University) ,  Ye, Xin (Key Laboratory of Space Launching Site Reliability Technology) ,  Tang, Qiu (School of Automation, Chongqing University)

Abstract ▼ AI-Helper 아이콘AI-Helper

Abstract A novel methodology based deep belief networks and multiple models (DBNs-MMs) is presented to accomplish fault detection for complex systems. And firstly, historical datasets are collected and processed to train the DBNs, so that DBNs can be constructed to learn the nonlinear dynamic chara...

주제어

#Fault detection   #Complex system   #Deep belief networks   #Multiple models   #Adaptive threshold   #Cryogenic propellant loading system  

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