The centralized online monitoring technology plays the most important role in nuclear power plants for the safety of major equipments and economic operation. In order to solve the false alarm and alarm failure problems in the traditional online monitoring, a new artificial intelligence monitoring method based on the local outlier factor and artificial neural networks model is put forward in this paper. This method is one of the multiple parameter dynamic threshold detection method. Firstly, a group of monitoring parameters of equipment is selected by analyzing the failure modes and failure phenomena of equipment. Secondly, enough data of this group of parameters needs to be collected and the abnormal data needs to be screened out. Thirdly, all the selected data is used to calculate the local outlier factor, and then the neural network model will be established by inputting the selected data and the local outlier factor. Finally, the neural network model can be used to assess the equipment health index with the real-time data of equipment parameters as input, and the health index represents the real-time health of equipment. In this paper, this method is used to develop a monitoring model of circulating water pump. In order to verify the validity of the model, enough monitoring data of healthy equipment and malfunction equipment are used to verify the monitoring results. The results show that the method can provide a pre-alarm for the early failure of the equipment with low false alarm rate, greatly improving the monitoring efficiency. © 2021, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.
