Abstract
A fault diagnosis can quickly and accurately diagnose the cause of a fault. Focusing on the characteristics of nuclear power plants (NPPs), this study proposes a distributed fault diagnosis method based on a back propagation (BP) neural network and decision tree reasoning. First, the fault diagnosis was carried out using the BP neural network and decision tree reasoning, and then a global fusion diagnosis was performed by fusing the resulting information. Second, the key technologies of the BP neural network and decision tree sample construction were studied. Finally, the simulation results show that the proposed distributed fault diagnosis system is highly reliable and has strong diagnostic ability, enabling efficient and accurate diagnoses to be realized. The distributed fault diagnosis system for NPPs provides a solid foundation for future research.
Highlights
Nuclear power plants (NPPs) produce a large number of monitoring signals
The fault diagnosis can be used to obtain key signals from a large amount of data, allowing the current operation state of the NPP to be determined. This is the key technology for improving the NPP intelligence (Elnokity et al, 2012)
The diagnosis results of the back propagation (BP) neural network method and decision tree model method are shown in Figures 18, 19
Summary
Nuclear power plants (NPPs) produce a large number of monitoring signals. For example, a typical alarm system has nearly 2,000 alarms (Mo et al, 2007). The fault diagnosis can be divided into data-driven, signal processing, and model-based methods (see Figure 2) (Ma and Jiang, 2011). With the application of knowledge graph, it is difficult to obtain a complex model of NPPs. this study seeks machine learning of threshold method (less data dependence) to complete the fault diagnosis.
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