Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach

Abstract

In this paper, a CMAC neural network application on fault diagnosis of a large air-conditioning system is proposed. This novel fault diagnosis system contains an input layer, binary coding layer, and fired up memory addresses coding unit. Firstly, we construct the configuration of diagnosis system depending on the fault patterns. Secondly, the known fault patterns were used to train the neural network. Finally, the diagnosis system can be used to diagnose the fault type of air-conditioning system. By using the characteristic of self-learning, association and generalization, like the cerebellum of human being, the proposed CMAC neural network fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. Furthermore, the following merits are obtained: 1) High accuracy. 2) High noise rejection ability. 3) Suit to multi-faults detection. 4) Memory size is reduced by new excited addresses coding technique.