Intelligent Diagnosis in Hydraulic Impact Fault by Combined Improved EEMD with SVM

Abstract:

Aims at the difficult to measure and influence serious in hydraulic impact fault, the intelligent diagnosis of hydraulic impact fault is proposed with improved EEMD and SVM in this paper. Three states of normal and sudden stop and suddenly reversing shock are set up on the hydraulic experimental bench. The improve EEMD is proposed by EEMD noise reduction, SVM extension signal, cubic spline interpolation improvement, and related pseudo components eliminating. The intelligent fault diagnosis in hydraulic system is researched by the improve EEMD to extract the IMF energy as feature vector, and the SVM training classification. Because distinguish and clear between normal state samples with the two impact fault samples, classification results are very right under a linear, polynomial kernel function, or a RBF, sigmoid and precomputed kernel function.

• Periodical: Applied Mechanics and Materials (Volumes 631-632)
• Pages: 553-557
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.631-632.553
• Online since: September 2014
• Authors: Mei Jun Zhang, Kai Jun Cai, Jian Tang, Jie Huang, Kai Chai
• Keywords: Classification Identification, EEMD, Intelligent Diagnosis, SVM

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