Deep convolutional neural network model based chemical process fault diagnosis.
Computers & Chemical Engineering(2018)
摘要
•A deep convolutional neural network model based fault diagnosis method is proposed for chemical processes.•A deep convolutional neural network model is constructed and applied in the Tennessee Eastman process.•An average fault diagnosis rate of 88.2% is achieved.•The model tuning and the dynamic diagnostic performance are explored.
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关键词
Fault diagnosis,Deep convolutional neural network,Alarm management,Tennessee Eastman process
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