Aviation direct-current converter online fault combined prediction method based on fractional order wavelet transformation

Abstract

The invention discloses an aviation direct-current converter online fault combined prediction method based on fractional order wavelet transformation. The method includes: (1) monitoring and collecting output voltage signals of the aviation direct-current converter in real time, calculating output voltage change rate of different moments, and using the output voltage change rate as converter performance degradation parameters; (2) conducting abnormal value rejection and missing data filling on performance degradation data by using a 3 sigma method and an interpolation method; (3) conducting fractional order wavelet transformation on the performance degradation data, the performance degradation data is decomposed into subcomponents with different scales, and determining noise components and removing the noise components by calculating a combination entropy between high-frequency components and environment data; (4) building a predication model of the high-frequency components in decomposition data by using a wavelet neural network, building a prediction model of low-frequency components by using a gray neural network, and conducting time sequence prediction; and (5) stacking predication values of the high-frequency components and the lower-frequency components to obtain a final predication value, conducting performance evaluation and fault predication on the aviation direct-current converter by combining fault threshold values. The aviation direct-current converter online fault combined prediction method removes disturbances caused by environment factor fluctuation in performance degradation data, restores real performance degradation data, simultaneously decomposes the performance degradation data into different frequency subcomponents with strong regularity, predicts the subcomponents by using a combined prediction model, enables prediction risks to be dispersed, and improves online fault prediction correctness.

Description

technical field [0001] The invention relates to the realization of an online fault prediction algorithm and method for an aviation DC converter, in particular to a new method for fault prediction of an aviation DC converter based on fractional wavelet transform and combined prediction. Background technique [0002] With the increasing number of aircraft electrical equipment, the power consumption of airborne electronic equipment has increased significantly, which puts forward higher requirements for the power supply quality of the aviation power system. Aviation power system mainly includes main power supply, auxiliary power supply, emergency power supply and secondary power supply. The secondary power supply converts the electrical energy form of the main power supply into various electrical energy forms required by electrical equipment, and is an indispensable part of the aviation power supply system. Aviation DC converter, as an important secondary power supply, is widel...

AI Technical Summary

Problems solved by technology

[0004] At present, there are few studies on aviation DC converters as fault prediction objects. In the actual use of aviation DC converters, their performance degradation data are usually complex nonlinear and non-stationary time series, which not only include the overall degradation trend, but also Contains various random fluctuation components and the amount of noise that varies with environmental factors

It is difficult to accurately predict it using a single model, and the prediction results contain noise items, which will also cause prediction uncertainty

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