Automated trend diagnosis using neural networks

This paper present a new method for trend diagnosis system using neural networks. Most of dynamical systems are not easy to analyze and detect faults because the observed parameters are not directly expressing the state of the system. We have to measure the temporal tendencies of the parameters, which is not easy not only for testing machine but also for human work. Here, the effectiveness of the trend fault diagnosis system using recurrent neural networks is examined for the air-conditioning system. The network was trained with the fault and correct data sequences obtained from the system simulation. The experimental fault detection results by using actual data proved that the proposed method is effective to perform the trend diagnosis of dynamic system.