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- W1996255890 abstract "Support vector data description (SVDD) has been applied to performance degradation assessment for years. But single kernel may not describe the varying distribution very well. Multi-kernel learning (MKL) method was developed and proved to perform better than single kernel. Previous studies have been conducted to build up a fixed model, which takes the sample distance as the assessment index. However, different condition may have the same distribution in feature space. In this paper, we proposed a new robust method for bearing performance degradation assessment based on multi-kernel SVDD, and designed a new index with hyper-sphere radius. The experiment results show that the new index can reflect the degradation's development exactly." @default.
- W1996255890 created "2016-06-24" @default.
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- W1996255890 date "2012-08-01" @default.
- W1996255890 modified "2023-09-24" @default.
- W1996255890 title "A novelty degradation assessment method for equipment based on multi-kernel SVDD" @default.
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- W1996255890 doi "https://doi.org/10.1109/coase.2012.6386504" @default.
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