A support vector machine fault prediction method based on adaptive ant lion optimization

A support vector machine and fault prediction technology, which is applied in the direction of prediction, data processing applications, computer components, etc., can solve problems such as large impact on prediction results

Pending Publication Date: 2019-05-17
BEIHANG UNIV
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  • Application Information

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Problems solved by technology

However, the type of model kernel function and the selection of parameters in the kernel function have a great influence on the prediction results.

Method used

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  • A support vector machine fault prediction method based on adaptive ant lion optimization
  • A support vector machine fault prediction method based on adaptive ant lion optimization
  • A support vector machine fault prediction method based on adaptive ant lion optimization

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Embodiment example

[0077] The key components in the main circuit of the DC-DC switching power supply include inductors, aluminum electrolytic capacitors, power switching devices, etc. Aluminum electrolytic capacitors are the most prone to failure in DC-DC switching power supplies, and most of the failures of DC-DC switching power supplies are caused by it.

[0078] The change of the electrical parameter characteristics of the aluminum electrolytic capacitor in the DC-DC switching power supply circuit will affect the change of the circuit output ripple voltage value, so the ripple voltage can be measured to reflect the change process of the health state of the DC-DC switching power supply. The proposed method is verified by taking the peak-to-peak data of the ripple voltage at the output of a Buck switching power supply as an example when the ESR degrades.

[0079] Step 1: Extract the peak-to-peak data of the ripple voltage at the output terminal when the ESR is degraded, as shown in Table 1.

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Abstract

The invention discloses a support vector machine fault prediction method based on adaptive ant lion optimization. The method comprises the following steps of step 1, preprocessing is carried out; step2, ant lion parameters are initialized; step 3, a support vector machine regression model is trained; step 4, fitness is calculated according to a test set; and step 5, a life curve is predicted by using a single-step loop iteration method. According to the method, the historical data in a period of time when degradation of a monitoring circuit occurs serve as training samples, the number of thesamples is limited, at the moment, when a traditional multi-step prediction method is adopted for predicting characteristic parameter values, a plurality of prediction values at the future moment areestimated at the same time, and prediction errors are gradually increased along with increase of the prediction distance. According to the method, a new predicted value is used for replacing an initial value at an earlier moment in each cycle to serve as a training sample, the sample is updated in real time, the accumulative errors are reduced, and the error value of each prediction is reduced tothe minimum, so that the alculation results are more accurate.

Description

Technical field: [0001] The invention relates to a single-step loop iterative fault prediction method (IAALO-SVM) based on an adaptive antlion optimized support vector machine. By optimizing the parameters in the support vector machine, the degradation of electronic products under different conditions can be obtained Lifetime belongs to the technical field of system engineering system reliability. Background technique: [0002] Support vector machine is a machine learning method based on statistical learning developed in recent years. It is based on statistical learning theory, starting from a small sample, using the structural risk minimization criterion, while minimizing the error of the sample point, considering the structural factors of the model, and fundamentally improving the generalization ability. At present, it is mainly used in pattern classification and nonlinear regression problems. Due to its superior learning ability, it has attracted more and more attention ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06N3/00
Inventor 胡薇薇范慧刘佳敏孙宇锋赵广燕
Owner BEIHANG UNIV
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