A stochastic degradation modeling based adaptive prognostic approach for equipment

被引：0
|
作者：
Sun, Guo-Xi [1 ]
Zhang, Qing-Hua [1 ]
Wen, Cheng-Lin [2 ]
Duan, Zhi-Hong [1 ]
机构：
[1] Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory, Guangdong University of Petrochemical Technology, Maoming, 525000, Guangdong
[2] School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 06期
关键词：
Bayesian method; Degradation; Expectation maximization; Lifetime prognosis;
D O I：
10.3969/j.issn.0372-2112.2015.06.013
中图分类号：
学科分类号：
摘要：
Current prognostic studies are usually based on historical degradation data, which are collected off line from different devices in a population with the same type. However, such data are not always available in practice. Toward this end, this paper presents a degradation modeling based adaptive remaining useful life prediction method for equipments in service. In the presented method, we use an exponential-like stochastic degradation model to represent the degradation process of equipments. Then, based on the monitored data during the degradation process, Bayesian approach is applied to update the stochastic parameters in the model, so the probability distribution of the predicted remaining useful life is derived as well as its point estimation. Differing from current studies, all unknown non-stochastic parameters in the model are estimated by expectation maximization algorithm, without requiring historical degradation data of multiple devices. Finally, numerical simulations and case study results substantiate the superiority of the presented method in predicting the remaining useful life. ©, 2015, Chinese Institute of Electronics. All right reserved.
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页码：1119 / 1126
页数：7
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