title: Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification.
abstract: To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.
authors.affiliation: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China ; School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Li
authors.affiliation: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Yongying
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Xiang
mesh_terms: D000465:Algorithms; D004868:Equipment Failure; D019544:Equipment Failure Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013018:Sound Spectrography / Q000379:methods*; D060388:Support Vector Machine; D014732:Vibration; D058067:Wavelet Analysis