Analogue circuit failure prediction method

Abstract

The invention discloses an analogue circuit failure prediction method which comprises the following steps: performing Monte Carlo analysis on various elements of an analogue circuit in a failure-free section and extracting various frequency band signal energy, normalizing the extracted frequency band signal energy to obtain a feature vector; training a BP neural network; judging failure modes with occurrence trends, extracting a failure prediction feature vector when the element is at the initial value, extracting the failure prediction feature vector when a detected circuit is in work, computing the cosine angle distance to represent the health degree of the element, computing the health degree threshold value when the element is in failure, and optimally selecting a kernel function width factor of a relevance vector machine algorithm, and performing the failure prediction on the analogue circuit. The method can be used for a real-time system, and can be further used for a non-real-time system, a failure of the linear analogue circuit can be predicted, and a failure of the non-linear analogue circuit can be predicted, and failures of main elements such as resistor, inductor and the capacitor in the analogue circuit can be predicted.

Description

technical field [0001] The invention relates to a fault prediction method of an analog circuit, in particular to a method for establishing a fault prediction model to predict faults of an analog circuit. Background technique [0002] Analog circuits are widely used in household appliances, industrial production lines, automobiles, aerospace and other equipment. The failure of analog circuits will cause performance degradation, functional failure, slow response and other electronic failures of the equipment. Therefore, it is very necessary to evaluate the state of the analog circuit. [0003] The state assessment of analog circuits generally includes fault diagnosis and fault prediction. Among them, fault diagnosis develops rapidly, and the accuracy rate of fault diagnosis can reach about 99% in a large number of research work. The current research results of fault prediction of analog circuits are generally aimed at specific components of analog circuits, rather than the w...

AI Technical Summary

Problems solved by technology

One difficulty with not being able to make predictions for circuits as a whole is that there are few methods that can accurately describe the degradation, i.e., health degradation, of individual components of an analog circuit

At the same time, there are currently few methods to predict the failure of nonlinear analog circuits

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