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Anfis model for vibration signals based on aging process in electric motors

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Abstract

In this study, the aging process of an electric motor is accomplished by adaptive neuro-fuzzy inference system (ANFIS) using vibration signals. Different ANFIS models are compared for representing the aging process in the best possible way. An artificial aging experiment is performed and vibration data taken from the initial (healthy) and final (faulty) cases are used to identify the aging process. Four different ANFIS models are presented. Moving average (MA) filters are applied to the input and output pairs for different lagging factors to change the smoothness degree of the data and thus the performance of system identification. The success of the models is evaluated on three conditions; the performance of the ANFIS and the linear correlation between expected output (faulty case data) and aging model output, in time and frequency domains.The study also evaluates the influence of preprocessing using MA filtering on the ANFIS performance for vibration data which have stochastic characteristics.

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Acknowledgments

The simpler version of this study is presented on The 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012). The authors would present their special thanks to Prof. B.R. Upadhyaya from the Maintenance and Reliability Center and Nuclear Engineering Department of University of Tennessee Knoxville, USA, for permission to the use of experimental data.

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Authors and Affiliations

  1. Electrical Engineering Department, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey

    Duygu Bayram & Serhat Şeker

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  1. Duygu Bayram
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  2. Serhat Şeker
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Correspondence to Duygu Bayram.

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Communicated by V. Loia.

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Bayram, D., Şeker, S. Anfis model for vibration signals based on aging process in electric motors. Soft Comput 19, 1107–1114 (2015). https://doi.org/10.1007/s00500-014-1326-5

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