Nuclear Science and Engineering / Volume 124 / Number 2 / October 1996 / Pages 339-348
Techniacl Paper / dx.doi.org/10.13182/NSE96-A28583
The early detection of incipient failures is of paramount importance for the safety and reliability of nuclear power plants. The feasibility of using artificial neural networks as process simulators in a fault detection device is explored. Two neural networks are trained to follow the dynamic evolution of the system pressure in a nonfaulty pressurizer of a pressurized water reactor. During an accident, the discrepancy between the plant’s signals and the neural networks’predictions can be used to rapidly detect the faulty condition. In reality, the signals will be unavoidably affected by a certain level of noise. The robustness of neural networks to noisy patterns assures a satisfactory degree of accuracy in the process predictions and, therefore, a high efficiency in the detection as well.