Nuclear power device fault diagnosis method based on local linear embedding and K-nearest neighbor classifier

Abstract

The invention provides a nuclear power device fault diagnosis method based on local linear embedding and a K-nearest neighbor classifier. The method comprises steps of (1) acquiring operation data of a nuclear power device in steady-state operation and typical accident states as training data; (2) using the mean-variance standardization method, carrying out dimensionless standardization processing on the training data to obtain high-dimension sample data; (3) using the local linear embedding algorithm, extracting low-dimension manifold structures of the high-dimension sample data so as to obtain low-dimension characteristic vectors; (4) inputting the low-dimension characteristic vectors into a K-nearest neighbor classifier to carry out classification training; (5), acquiring real-time operation data of the nuclear power device, and repeating the steps of (2) and (3); and (6) using the trained K-nearest neighbor classifier to make decisions for classification of the characteristic vectors. According to the invention, by taking advantages of the nonlinear manifold learning method in the aspects of characteristic dimension reduction extraction, the provided method is suitable for fault diagnosis of nonlinear data high-dimension systems, and has quite high fault diagnosis accuracy.

Description

technical field [0001] The invention relates to a fault diagnosis method for a nuclear power plant. Background technique [0002] A nuclear power plant is a complex dynamic time-varying system with potential radioactive hazards. Once a failure or accident occurs, it may cause serious radiological consequences. Due to its particularity, nuclear power plants have high requirements on the ability and quality of operating personnel. Once an operation error may cause heavy losses, it is difficult for operators to make completely correct judgments and behaviors under tremendous psychological pressure. Fault diagnosis technology can judge the possible fault type, fault location and fault degree according to the change of system parameters, and assist the operator to judge the real state of the nuclear power plant and take reasonable operations, so as to minimize the fault loss . Therefore, online fault diagnosis research on nuclear power plant is an important means to ensure the...

AI Technical Summary

Problems solved by technology

The feature extraction and data dimension reduction methods currently applied to nuclear power plants are all linear methods, which cannot meet the requirements of nuclear power plant fault diagnosis

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