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A-Smooth Regularization for Ill-Posed Equations with Perturbed Operators and Noisy Data 被引量:1
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作者 张宁 贺国强 《Journal of Shanghai University(English Edition)》 CAS 2003年第1期35-40,共6页
This paper concerns the A smooth regularization method for linear ill posed equations in the presence of perturbed operators and noisy data. The semi and full a posteriori Morozov discrepancy principles for... This paper concerns the A smooth regularization method for linear ill posed equations in the presence of perturbed operators and noisy data. The semi and full a posteriori Morozov discrepancy principles for choosing the regularization parameter are proposed, which lead to satisfactory results. 展开更多
关键词 ill posed equations A smooth regularization Morozov discrepancy principle convergence rate.
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Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis 被引量:2
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作者 HU Yun-yun ZHAO Chun-hui KE Zhi-wu 《Journal of Central South University》 SCIE EI CAS CSCD 2021年第12期3838-3855,共18页
Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in gen... Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process. 展开更多
关键词 fault prognostic NONSTATIONARY industrial process fault degradation-oriented slow feature analysis(FDSFA) temporal smoothness regularization
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