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Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery

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Abstract

In practical situations, the vibration collected from rotating machinery is often a mixture of many vibration components and noise; therefore, it is very necessary to extract fault features from the mixture first in order to achieve effective rotating machinery fault diagnosis. In this paper, independent component analysis with reference method is proposed to extract the fault features using reference signals established based on the a priori knowledge of machine faults; experimental studies based on both simulated and actual fault signals of rotating machinery have been performed; and the results show that the proposed approach can effectively extract fault features under the situation of interferences and coexistence of multiple faults.

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Acknowledgments

The Project Supported by Guangdong Natural Science Foundation (Grant No. S2011010004143) and the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.2009131).

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Authors and Affiliations

  1. Shenzhen Engineering Lab of Industrial Robots and Systems, Harbin Institute of Technology (HIT) Shenzhen Graduate School, Shenzhen, 518055, Guangdong, People’s Republic of China

    Gang Yu

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  1. Gang Yu
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Correspondence to Gang Yu.

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Yu, G. Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery. Neural Comput & Applic 26, 187–198 (2015). https://doi.org/10.1007/s00521-014-1726-6

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  • DOI: https://doi.org/10.1007/s00521-014-1726-6

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