Journal
IEEE ACCESS
Volume 6, Issue -, Pages 75813-75823Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2882801
Keywords
Feature reduction; gearbox; fault diagnosis; principal component analysis; locally linear embedding; hybrid algorithm
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Funding
- Program of Chongqing Municipal Education Commission [KJZH17123]
- National Key Research & Development Program of China [2016YFE0132200]
- Open Funding of Chongqing Technology and Business University [KFJJ2017076]
- Research Start-Up Funds of Chongqing Technology and Business University [1856018]
- Graduate Scientific Research Innovation Project of Chongqing Technology and Business University [yjscxx2018-060-15]
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The dimensionality reduction of the high-dimensional feature space is a critical part for data preprocessing, which directly affects the accuracy of fault diagnosis. In this paper, a novel hybrid algorithm named principal component locally linear embedding (PCLLE) is introduced to compress the original high-dimensional feature. This approach combines the optimization objectives of the principal component analysis (PCA) and locally linear embedding (LLE), which attempts to find a mapping that meets the optimization goals of PCA and LLE at the same time. It is applied on the gearbox fault diagnosis. In the experiment, the extracted fault-sensitive feature is compressed by PCLLE method. Then, the compressed feature is embedded with five classifiers for fault detection. To evaluate the performance of the proposed new method, the traditional PCA and LLE methods are introduced for comparison. Experimental results show that the PCLLE algorithm has good performance during the classification process compared with the traditional PCA and LLE method.
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