Automated trend diagnosis using neural networks

The paper presents a new method for a trend diagnosis system using neural networks. Most dynamical systems are not easy to analyze and faults are difficult to detect because the observed parameters do not directly express the state of the system. We need to measure the temporal tendencies of the parameters, which isn't easy for testing machines or humans. The effectiveness of the trend fault diagnosis system using recurrent neural networks is examined for an air-conditioning system. The network was trained with faulty and correct data sequences obtained from system simulation. The experimental fault detection results using actual data proved that the proposed method is effective for performing trend diagnosis of dynamic systems.