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| Individual residual life prediction method of electronic components based on model fusion |
| Zhao Changdong1,2, Xiang Shihu1,2, Wang Yao1,2 |
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China |
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Abstract Electronic components are usually the weak parts of a system, and they are generally required to fulfill critical tasks. If a seriously degraded electronic component is not replaced in time, its failure may result in the failure of the whole system. Therefore, it is significant to accurately predict the residual life of electronic components in the system. Due to factors such as complex failure mechanism, little prior information, and insufficient monitoring data, the degradation model of an electronic component is usually uncertain, which may cause an inaccurate prediction of the residual life. For the problem of the model uncertainty, the existing studies did not consider the predictive ability of a model, and required abundant prior information or degradation data. To solve this problem, a residual life prediction method for an individual electronic component based on model fusion is proposed in this paper. Generally speaking, alternative models are fused based on a novel model evaluation index that incorporates the fitting ability, complexity, and prediction accuracy of a model, and then the residual life of an electronic component is predicted based on the fused model.
The proposed residual life prediction method is focused on the case that the performance of an electronic component can be characterized by a single performance parameter. Firstly, the set of alternative degradation models is established for typical and common degradation patterns of electronic components. Secondly, the set of alternative residual life distribution models is derived based on the set of alternative degradation models. Thirdly, the maximum likelihood estimation method is adopted to estimate the unknown parameters in the alternative models by utilizing the available degradation data collected up to the current time. Fourthly, the probability that an alternative model is the true one is constructed according to the proposed novel model evaluation index, and then the alternative residual life distribution models are fused via the law of total probability to obtain the fused model of the residual life distribution. Fifthly, the expected residual life is taken as the point estimate of the residual life, and it is calculated based on the fused residual life distribution model.
Three real cases are carried out to demonstrate the effectiveness and practicability of the proposed residual life prediction method. Through comparison analysis, it is shown that the proposed method is superior to the methods that predict the residual life based on the best model selected according to a traditional model evaluation index, such as Akaike information criterion (AIC) and the value of the likelihood function, or the proposed index. Moreover, it is verified that the proposed method can ensure a favorable prediction accuracy, even if the regularity of the degradation data is poor and the amount of the data is small.
Some specific conclusions are summarized as follows. 1) The proposed novel model evaluation index is a comprehensive index, since it simultaneously takes the fitting ability, complexity, and prediction accuracy of a model into account. 2) The strategy of model fusion can not only reduce the risk of abandoning a model with better prediction result but also lead to a fused model that has the characteristics of all the alternative models, which can overcome the drawbacks of using a single model to a certain extent. 3) The proposed residual life prediction method for electronic components does not depend on a large amount of prior information or degradation data, and has a high prediction accuracy and a powerful practicability.
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