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        Gearbox health condition identification by neuro-fuzzy ensemble

        Long Zhang,Guoliang Xiong,Leping Liu,Qingsong Cao 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.3

        A neuro-fuzzy ensemble (NFE) model has been investigated for machinery health diagnosis. The proposed diagnosis system was illustrated by discriminating between various gear health conditions of a motorcycle gearbox. Four different health scenarios were considered in this work: normal, slight-worn, medium-worn and broken-teeth gear. Experimental results show the NFE model per-forms better than single neuro-fuzzy (NF) model with respect to classification accuracy, sensitivity and specificity, while the computational complexity is not increased significantly. In addition, the NF-based models are able to interpret their reasoning behavior in an intuitively understandable way as fuzzy if-then rules, which allows users to gain a deep insight into the data.

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