Peng Minjun

A Hybrid Approach for Fault Diagnosis based on Multilevel Flow Models and Artificial Neural Network

Proper and rapid identification of malfunctions is of premier importance for the safe operation of nuclear power plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnosis system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, multilevel flow models (MFM) and artificial neural network (ANN) are proposed and employed to develop a fault diagnosis system, with the intention of improving the successful rate of identification on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted from a newly developed full-scale NPP simulator for evaluating the performance of the proposed hybrid approach. This paper will introduce the proposed hybrid approach, simulation experiment and its results.

A cascade intelligent fault diagnostic technique for nuclear power plants

ABSTRACT Safe operation of nuclear power plant is one of the most important tasks in nuclear power development. This justifies the variety of methods that have been proposed to support the operators in the task of plant condition monitoring, fault detection, and diagnosis. A number of hybrid fault detection and diagnosis methods have also been proposed, with their attendant weaknesses. This work proposes the hybrid of principal component analysis (PCA), signed directed graph (SDG), and Elman Neural Network (ENN) for fault detection, fault isolation, and severity estimation, respectively. The proposed hybrid method is verified with the data derived from Personal Computer Transient Analyzer (PCTRAN) simulation. The verification result shows that the PCA-based fault detection methodology realized timely detection of anomaly in the simulated nuclear power plants system, the SDG-based fault recognition method was able to isolate the system abnormality and identify the root causes, and the ENN-based fault severity estimation method presents the failure fraction of fault, representing the severity. With this integrated hybrid method, more fault information is provided for the operators, which serves as a good foundation for further decision-making and interventions.

Research of Software User Interface Evaluation Method based on Subjective Expectation

The artificial neural network based subjective expectation evaluation method on software user interface is proposed. The analytic hierarchy process and radial basis functions neural network based subjective expectation evaluation models are established. The two kinds of models are used to evaluate the control system software user interface of power station. Evaluation results show that the radial basis function neural network based evaluation method can be used to evaluate the software user interface. Because the proposed method has the advantages of deciding the subjective expectation evaluation weights automatically, so it can observably enhance the objectivity of subjective expectation evaluation of software user interface.