Abstract
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 performs 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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Recommended by Associate Editor Sung Hoon Ahn
Long Zhang is currently a lecturer at Mechatronics Engineering School of East China Jiao Tong University, China. He received his PhD degree from Shanghai Jiaotong University, China, in Jan. 2011. His current research interests focus on vibration analysis, signal processing, artificial intelligence and their applications to machinery condition monitoring and fault diagnosis.
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Zhang, L., Xiong, G., Liu, L. et al. Gearbox health condition identification by neuro-fuzzy ensemble. J Mech Sci Technol 27, 603–608 (2013). https://doi.org/10.1007/s12206-013-0112-0
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DOI: https://doi.org/10.1007/s12206-013-0112-0