Research and application of multifractal approximate entropy and subtractive FCM clustering

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (18) : 205-209.

ZHANG Shu-qing, LI Pan , HU Yong-tao ，WANG Jia-sen , JIANG Wan-lu

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Abstract

A feature extraction method based on multifractal and approximate entropy is presented, which is used for gearbox fault signal. Because of the complexity of gearbox fault data, firstly subtractive clustering is used to obtain the initial cluster center of characteristics. Then fuzzy c-means clustering(FCM) is used for further processing to achieve automatic gearbox fault diagnosis and identification．The volatility of a signal can be expressed by feature values extracted by multifractal spectrum, such as spectral width; and the complexity of the signal can be represented by approximate entropy(ApEn)．Combination of above obtains patterns of gearbox faults more accurately．Problem that FCM is easily fallen into local optimum can be effectively solved by subtractive fuzzy clustering, which also improves convergence rate. Characteristic parameters are processed by clustering analysis. In order to achieve gearbox fault clustering and recognition, the membership grade of data points and cluster center is calculated. To prove the feasibility and effectiveness of the method proposed in this paper, wind turbine gearbox fault diagnosis experiment is implemented. This paper provides a new effective way for gearbox fault diagnosis.

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mutifractal / approximate entropy / subtractive fuzzy clustering / fault diagnosis

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ZHANG Shu-qing, LI Pan,HU Yong-tao,WANG Jia-sen,JIANG Wan-lu. Research and application of multifractal approximate entropy and subtractive FCM clustering[J]. Journal of Vibration and Shock, 2015, 34(18): 205-209

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