Sensor fault detection in nuclear power plant using auto-associative neural network

• Fault diagnosis system usually includes a prediction module and a diagnosis module. The prediction module is supposed to predict sensor values based on sensor observations, and the diagnosis module is used to determine whether a sensor has degradation or failure by comparing the difference between sensor observations and predictions. Auto-Associative Neural Network (AANN) is a widely applied data-driven prediction method consisting of input layer, mapping layer, bottleneck layer, de-mapping layer and output layer. AANN compresses the information contained in the input data (i.e. sensor observations) into bottleneck layer through mapping layer, and then reconstructs the sensor values at output layer based on the extracted nonlinear features stored in bottleneck layer through de-mapping layer. This paper focuses on the feasibility study of AANN model in fault diagnosis for nuclear power plants. The performance of AANN model was studied by using simulation data, and the AANN model was optimized by combining prejudgment and secondary prediction. The results show that the optimized AANN model can improve the performance comparing to original AANN model, and can be applied to the fault diagnosis for nuclear power plant sensors.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Auto-associative neural network

Fault diagnosis

Nonlinear feature extraction

Robust model

Sensitivity

Failure analysis

Forecasting

Mapping

Multilayer neural networks

Nuclear energy

Nuclear fuels

Nuclear power plants

Autoassociative neural networks

Fault diagnosis systems

Feasibility studies

Nonlinear features

Robust modeling

Sensor fault detection

Fault detection

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