Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

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• 作者：Francisco Serdio^a ; ^{francisco.serdio@jku.at} ; Edwin Lughofer^a ; ^{edwin.lughofer@jku.at} ; Alexandru-Ciprian Zavoianu^a ; ^{ciprian.zavoianu@jku.at} ; Kurt Pichler^b ; ^{kurt.pichler@lcm.at} ; Markus Pichler^b ; ^{markus.pichler@lcm.at} ; Thomas Buchegger^b ; ^{thomas.buchegger@lcm.at} ; Hajrudin Efendic^c ; ^{hajrudin.efendic@gmx.at}
• 关键词：Fault detection ; System identification ; Hybrid memetic fuzzy modeling ; GenSparseFIS ; Residual signals ; Adaptive filters ; Multi-sensor networks
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：51
• 期：Complete
• 页码：60-82
• 全文大小：3476 K
• 卷排序：51

文摘

Fault detection framework based on data-driven system identification, applicable for large-scale sensor networks. Hybrid memetic learning method for Takagi–Sugeno fuzzy systems (combining sparse with heuristics-based optimization). Parameter and structural solutions closer to optimality inducing higher predictive quality of fuzzy models. Adaptive filter design for incrementally smoothening residual signals in a data-streaming context (single-pass). Significant improvement of fault detection rates over state-of-the-art while ensuring very low false positive rates.