Fuzzy nearest neighbor fusion diagnosis method of thermodynamic system fault

Abstract

The invention, which belongs to the technical field of the fault diagnosis of the thermodynamic system, discloses a fuzzy nearest neighbor fusion diagnosis method of a thermodynamic system fault. A feature parameter of a thermodynamic system fault is obtained when a set works on a rated condition and a normal value of the feature parameter on the rated condition is determined; standardization processing is carried out on the feature parameter by using a symptom calculation method and a typical fault prototype is obtained by using a gravitation searching algorithm; according to a measured value and the normal value of the feature parameter, a real-time fault symptom is calculated; similarity between the real-time fault symptom and the typical fault prototype is compared and a fuzzy nearest neighbor classifier provides a fault membership; on the basis of combination with the fault membership provided by the fuzzy nearest neighbor classifier, a neural network carries out secondary diagnosis to obtain a final diagnosis result. The method has advantages of fast diagnosis speed and high diagnosis precision and can be used for diagnosis of a thermodynamic system fault on a rated condition or different steady-state conditions; and with real-time prediction of the fault feature parameter, the method is also suitable for the thermodynamic system fault diagnosis during the variable-condition dynamic process.

Description

technical field [0001] The invention belongs to the technical field of thermal system fault diagnosis, in particular to a fuzzy nearest neighbor fusion diagnosis method for thermal system faults. Background technique [0002] The thermal system is an important system of a thermal power plant, which has an important impact on the safety and economy of the overall operation of the unit. Carrying out fault diagnosis research on the thermal system is crucial to improving the overall availability of thermal power units, and the safety, reliability and economy of unit operation. It has important theoretical and practical value. The thermal system is a complex series-parallel system composed of multiple strongly coupled subsystems. There are many parameters and mutual influences. The manifestations of faults are diverse. The fault characteristics vary greatly with random group loads and working conditions, resulting in an excessively large sample set of thermal system faults. The ...

AI Technical Summary

Problems solved by technology

The existing methods do not consider the effective reduction of the fault sample set, and only study the diagnosis method. The existence of a large number of redundant and noisy samples increases the storage requirements and calculation costs of the diagnosis system, which seriously affects the actual application effect of the diagnosis system in the field.

In addition, most of the existing methods are researched on a certain stable load condition (such as the rated load condition), and only a few literatures use neural networks to diagnose faults under different steady-state conditions.

At present, large-scale thermal power units need to participate in peak-shaving operation, and the random group load adjustment of the thermal system is often operated under variable conditions. The existing methods cannot adapt to this new situation, which greatly limits the practicability of the fault diagnosis system.

Images