Bearing Condition Identification Using Improved Multiscale Entropy and SVMs

This paper presents a method for bearing health condition identification based on improved multiscale entropy (IMSE) and support vector machines (SVMs). IMSE refers to the calculation of improved sample entropies, i.e., fuzzy sample entropies (FSampEn) across a sequence of time scales, which takes into account not only the nonlinearity but also the interactions and coupling between mechanical components, thus providing much more information regarding machine health condition compared to traditional single scale-based entropy. On the other hand, in engineering practice, the amount of fault samples is often limited, which thus decrease the performance of traditional classifiers like artificial neural networks (ANNs). Currently popular SVMs provide a favorable solution to small sample-sized problems. In this study, IMSE and SVMs were employed as fault feature extractor and classifier, respectively. The experimental results verify that the proposed method has potential applications in bearing health condition identification.