Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process. (April 2021)

• Record Type: Journal Article
• Authors: Zhang, Sen-Ju; Kang, Rui; Lin, Yan-Hui
• Is Part Of: Reliability engineering & system safety. Volume 208(2021)
• Publication Date: 2021-04
• Subjects: Uncertainty theory -- Recovery phenomenon -- Remaining useful life -- Epistemic uncertainty -- Degradation modeling; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107440
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Uncertain process is adopted for degradation modeling accounting for epistemic uncertainty. A novel similarity based-uncertain weighted least squares estimation method is proposed. A denoising method is proposed to deal with the noises caused by recovery phenomenon. The proposed framework is applied to real lithium-ion battery degradation dataset for demonstration. Abstract: Remaining useful life prediction based on degradation modeling is of great importance to condition-based maintenance, for which epistemic uncertainty due to the lack of sufficient knowledge needs to be characterized. For certain components, such as the batteries, the recovery phenomenon during degradation has to be considered, and the epistemic uncertainty associated with it is inevitable. This paper proposes a systematic method for degradation modeling and remaining useful life prediction based on uncertain process for degradation with recovery phenomenon. First, uncertain process is adopted for degradation modeling accounting for epistemic uncertainty. Then, a novel similarity based-uncertain weighted least squares estimation method is proposed to update the model parameters with real-time monitoring data. Afterwards, a denoising method is used to deal with the noises caused by recovery phenomenon. Finally, remaining useful life is calculated by uncertain simulation. A case study on real lithium-ion battery degradation dataset is performed to illustrate the effectiveness of the proposed method in comparison with traditional stochastic process.