Shahrood University of Technology Journal of Solid and Fluid Mechanics 2251-9475 8 3 2018 09 23 Gear Fault Detection Based on Best Feature Selection by Particle Swarm Optimization Gear Fault Detection Based on Best Feature Selection by Particle Swarm Optimization 49 64 1321 10.22044/jsfm.2018.6081.2449 FA A. Bagheri Prof., Dep. Of Mech. Eng., Guilan Univ., Rasht, Iran S. Nezamivand Chegini Ph.D. Student, Mech. Eng., Guilan Univ., Rasht, Iran. M. Ramezani Dashtemian MSc of Mechanical Engineering, Ahrar Institute of Technology and Higher Education, Rasht, Iran. F. Najafi Prof., Dep. Of Mech. Eng., Guilan Univ., Rasht, Iran. Journal Article 2017 08 13 In this paper, a new method is presented for gear fault detection. The vibrational signals of gearbox set are collected in three conditions: normal, chipped and worn teeth. These signals are adaptively decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD). Since, all of the IMFs drived from the EMD are not appropriate for fault detection, the cross-corrolation concept is used to select all most apptoptiate IMFs. Then, feature matrix corresponding to each condition, is extracted using statistical functions. “One-against-one” support vector machine (SVM-OAO) is utilized to classify the faults. Since, all of the extracted features are not suitable for fault detection and SVM has parameters to be set, the particle swarm optimization (PSO) is used to select the best feature and detect optimal parameters of SVM. Objective function in this paper is accuracy of the SVM classifier in predicting of gearbox condition. Obtained results show that the selected features in this method and optimized SVM have the excellent ability to classify the faults. In this paper, a new method is presented for gear fault detection. The vibrational signals of gearbox set are collected in three conditions: normal, chipped and worn teeth. These signals are adaptively decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD). Since, all of the IMFs drived from the EMD are not appropriate for fault detection, the cross-corrolation concept is used to select all most apptoptiate IMFs. Then, feature matrix corresponding to each condition, is extracted using statistical functions. “One-against-one” support vector machine (SVM-OAO) is utilized to classify the faults. Since, all of the extracted features are not suitable for fault detection and SVM has parameters to be set, the particle swarm optimization (PSO) is used to select the best feature and detect optimal parameters of SVM. Objective function in this paper is accuracy of the SVM classifier in predicting of gearbox condition. Obtained results show that the selected features in this method and optimized SVM have the excellent ability to classify the faults. https://jsfm.shahroodut.ac.ir/article_1321_acdbc2788cf1658da5613a2abce38663.pdf