Online intelligent fault prediction method for power electronic circuit based on RS-CMAC (rough sets and cerebellar model articulation controller)

Abstract

The invention discloses an online intelligent fault prediction method for a power electronic circuit based on RS-CMAC (rough sets and cerebellar model articulation controller) and belongs to the technical field of fault testing for power electronic circuits. The method includes: monitoring node signals in real time, performing wavelet denoising to obtain a fault feature sample, extracting circuit performance parameters, and building an RS-CMAC model to predict a time sequence of the circuit performance parameters in future time. According to the rough set theory and the CMAC model, input data of the CMAC model are simplified by a rough set data analysis method, and the efficiency in analyzing faults of the power electronic circuit is improved.

Description

technical field [0001] The invention discloses an RS-CMAC-based online intelligent fault prediction method for power electronic circuits, which belongs to the technical field of power electronic circuit fault testing. Background technique [0002] The power circuit in the airborne power electronic device is composed of power components. When placed in a certain space environment, its components are not only subjected to high-frequency start / stop operations, overvoltage, and overcurrent operations, but their performance is also very easy. Affected by external mechanical pressure (shock), EMI, ambient temperature / humidity, salinity and other stresses, which may cause changes in device parameters, if the changes are greater than the allowable range, it will often lead to circuit output performance Deterioration (for example: waveform quality deterioration, THD increase, etc.), and even cause output function failure, which seriously threatens flight safety and ultimately affects...

AI Technical Summary

Problems solved by technology

But whether it is the basic CMAC model or the fuzzy CMAC model, when the sample input dimension is large, the amount of calculation required is amazing

[0009] At present, combined with the use of cerebellar neural network to predict power electronic circuit faults, there is a problem of large amount of calculation. No scholar has proposed a method combining rough set data analysis and cerebellar neural network prediction method to predict power electronic circuit faults.

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