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Fault diagnosis model based on fuzzy support vector machine combined with weighted fuzzy clustering

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Abstract

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC). Considering the relationship between the sample point and non-self class, FC algorithm is applied to generate fuzzy memberships. In the algorithm, sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC. Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm, the penalty factor and kernel parameter of which are optimized by GA. Finally, the model is executed for multi-class fault diagnosis of rolling element bearings. The results show that the presented model achieves high performances both in identifying fault types and fault degrees. The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

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Authors and Affiliations

  1. State Key Laboratory of Engines, Tianjin University, Tianjin, 300072, China

    Junhong Zhang  (张俊红), Wenpeng Ma  (马文朋), Liang Ma  (马 梁) & Zhenpeng He  (何振鹏)

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  1. Junhong Zhang  (张俊红)
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  2. Wenpeng Ma  (马文朋)
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  3. Liang Ma  (马 梁)
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  4. Zhenpeng He  (何振鹏)
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Corresponding author

Correspondence to Junhong Zhang  (张俊红).

Additional information

Supported by the joint fund of National Natural Science Foundation of China and Civil Aviation Administration Foundation of China (No. U1233201).

Zhang Junhong, born in 1962, female, Dr, Prof.

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Zhang, J., Ma, W., Ma, L. et al. Fault diagnosis model based on fuzzy support vector machine combined with weighted fuzzy clustering. Trans. Tianjin Univ. 19, 174–181 (2013). https://doi.org/10.1007/s12209-013-1927-6

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  • DOI: https://doi.org/10.1007/s12209-013-1927-6

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