Artificial Neural Network Fault Diagnosis on the Heat Exchanger Fouling of Heat Pump Unit
Fang Jian-liang · Construction Machinery For Hydraulic Engineering & Power Station · 2005
Fault detection and diagnosis is an important method of improving safety and reliability of the system. Modeling of the heat exchanger is put forward in the paper. There are some pressure variations of the heat pump with the fouling. The heat change efficiency of the unit becomes lower. Fault data are gotten by the experiment. Using the strong abilities of artificial neural networks (ANN) in self-learning and mode distinguishing, the fault of heat exchanger fouling is identified through the perceptron ANN model which is suitable to the simple pattern classification.