Equipment performance degradation trend extraction and prediction method

Abstract

The invention relates to an equipment performance degradation trend extraction and prediction method. The method comprises the following steps: (1) establishing a hydroelectric generating set standardhealth model which comprehensively considers active power and a working head coupling effect and is based on inverse distance weighting, and obtaining a performance degradation time sequence of a current hydroelectric generating set according to acquired real-time online data of the hydroelectric generating set; (2) decomposing the performance degradation time sequence of the hydroelectric generating set into a sum of a plurality of stable PRC time sequences and a trend component by utilizing ITD; (3) performing complexity characteristic identification on all the obtained component time sequences, and reconstructing the components according to a preset complexity characteristic threshold to obtain a performance degradation trend; and (4) evaluating, judging and predicting the performanceof the hydroelectric generating set equipment according to the obtained performance degradation trend, and finding out equipment abnormity in time. The method can be widely applied to the field of hydroelectric generating set equipment performance degradation trend prediction.

Description

technical field [0001] The invention relates to a method for extracting and predicting the degradation trend of equipment performance, in particular to a method for extracting and predicting the degradation trend of equipment performance based on inverse distance weighting and ITD (Intrinsic Time Scale Decomposition). Background technique [0002] With the increase of accumulative running time, the performance of each equipment of the hydroelectric unit degrades continuously. How to effectively determine the true state of the unit and better predict the degradation trend of the unit, so as to detect the abnormality of the unit in time and arrange maintenance reasonably, is an important issue for the transformation of hydropower units from planned maintenance to condition-based maintenance. [0003] At present, the fault diagnosis technology of hydroelectric units cannot meet the needs of the field, the main reason is the complexity of hydroelectric unit faults and the lack o...

AI Technical Summary

Problems solved by technology

[0004] Vibration is the main failure factor that affects the normal operation of hydroelectric units and endangers the life of the unit. Excessive vibration will cause dynamic and static friction of the unit, accelerate the wear of parts, cause fatigue damage to parts, reduce the economy of the unit, and even cause damage to the unit in severe cases. serious accident

However, at present, most methods such as neural networks are used to directly predict the vibration time series of hydroelectric units. This method completely ignores the influence of unit operating conditions on vibration, and there are large errors and impracticalities.

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