Abstract
This paper proposes a genetic algorithm-based dimensionality reduction approach for reliable low-speed rolling element bearing fault diagnosis by exploiting both inter-class separability and intra-class compactness. In this study, multiple bearing defects under different load conditions are used to validate the effectiveness of the proposed dimensionality reduction methodology. In addition, the classification accuracy of the proposed approach is compared with that using two conventional dimensionality reduction techniques and the experimental results show that the proposed approach outperforms these methods, achieving an average classification accuracy of 94.8%.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-319-13560-1_95
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Nguyen, P., Kang, M., Kim, J., Kim, JM. (2014). Reliable Fault Diagnosis of Low-Speed Bearing Defects Using a Genetic Algorithm. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_20
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DOI: https://doi.org/10.1007/978-3-319-13560-1_20
Publisher Name: Springer, Cham
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