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• W1976619771 abstract "Considering the non-linearity existing in bearing vibration signals as well as the scarcity of fault samples, this paper presents a method for bearing health condition identification based on improved multi-scale entropy (IMSE) and support vector machines (SVMs). IMSE refers to the calculation of improved sample entropies (i.e. fuzzy sample entropies across a sequence of scales). Applying IMSE to mechanical vibration signals can take into account not only the non-linearity but also the interactions and coupling between mechanical components, thus providing much more information regarding the machine health condition than traditional single-scale entropy can be expected to. In engineering practice, the amount of fault samples is often limited for training a classifier, which thus decreases the performance of traditional classifiers like artificial neural networks (ANNs). SVMs are derived from statistical learning theory, which is different from the conventional statistical theory on which ANNs are based. SVMs provide a favourable solution to small sample-sized problems. In this study, IMSE and SVMs are employed as fault feature extractor and classifier, respectively. The experimental results verify that the proposed method has potential applications in bearing health condition identification." @default.
• W1976619771 created "2016-06-24" @default.
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• W1976619771 date "2010-06-01" @default.
• W1976619771 modified "2023-09-26" @default.
• W1976619771 title "Applying improved multi-scale entropy and support vector machines for bearing health condition identification" @default.
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• W1976619771 doi "https://doi.org/10.1243/09544062jmes1784" @default.
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