A fault prediction method based on iaalo-svm and similarity measure

Abstract

The invention discloses a fault prediction method based on IAALO-SVM and similarity measurement, the steps are as follows: step 1: select key fault components; step 2: extract circuit system characteristic parameters; step 3: train support vector machine regression model; step 4 : Calculate the similarity distance; Step 5: Perform lifetime fusion. The present invention combines measured data with offline data, conducts in-depth research on the time series similarity measurement method, effectively avoids the single-step loop iteration method of adaptive antlion optimization support vector machine, and can achieve more accurate predictions in a longer period of time However, it still has the disadvantage that the farther the prediction time is from the initial training sample, the larger the prediction error is, and the larger the error of the calculated remaining service life is. At the same time, according to the characteristics of the sequence data to be tested, the dynamic time warping method is selected to mine the similarity between the test object and the offline data, and the offline data is fully utilized to predict the fault of the test object.

Description

technical field [0001] The present invention relates to a kind of fault prediction method based on IAALO-SVM and similarity measurement, specifically a kind of single-step cycle iterative fault prediction method based on adaptive antlion optimized support vector machine (IAALO-SVM), through support vector The parameters in the machine are optimized to obtain the degradation life of electronic products under different conditions. The present invention uses the offline database of fault parameters of analog circuits and combines the measured data with offline data to propose a fault prediction based on IAALO-SVM and similarity measure new plan. Introducing the time series similarity measurement method to select similar databases, in order to fuse the fault prediction results according to the similarity, and realize the remaining life prediction of analog circuits, which belongs to the field of system engineering system reliability technology. Background technique [0002] Sup...

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Problems solved by technology

[0005] Although the self-adaptive antlion optimization support vector machine single-step loop iterative method can achieve more accurate predictions in a longer period of time, it still has the problem that the farther the prediction time is from the initial training sample, the larger the prediction error will be. And the error of the remaining service life is also larger

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