Amirkabir University of Technology Amirkabir Journal of Mechanical Engineering 2008-6032 54 8 2022 10 23 Monitoring and Troubleshooting Alstom Locomotive Blowers using Vibration Analysis and Support Vector Machine Monitoring and Troubleshooting Alstom Locomotive Blowers using Vibration Analysis and Support Vector Machine 1833 1850 4900 10.22060/mej.2022.21122.7393 FA Mahdi Zakizadeh Department of Mechanical Engineering, Guilan University, Guilan, Iran Ali Jamali Department of Mechanical Engineering, Guilan University, Guilan, Iran Mansoor Rafeeyan Faculty of Mechanical Engineering, Yazd University, Yazd, Iran Hamid Saeedi Faculty of Electrical Engineering, Yazd University, Yazd, Iran Ali Chaibakhsh دانشکده مهندسی مکانیک، دانشگاه گیلان، گیلان، ایران Journal Article 2022 03 15 Vibration analysis is one of the most practical methods for monitoring and troubleshooting rotating equipment. In this research, vibration analysis and support vector machine algorithms were used for monitoring and troubleshooting Alstom locomotive blowers. First, vibration data were collected from the blowers and the received signals were categorized into four groups: healthy blowers and blowers with problems of unbalance, loose shaft (base), and warped blades. Sixteen frequency and time features were then extracted from the received signals. Because in rotating systems, the ratio of the intensity of vibrations in the harmonics of the rotation of the machine can help diagnose the faults, the ratios of all features were calculated and defined as new features. The accuracy of the network can be sometimes lowered by the multitude of features, thus, a t-test filter was inserted into the support vector machine algorithm to select the features. The results show that the t-test filter increased the accuracy of the support vector machine algorithm. Finally, the feature selection of this network was compared with the feature selection by the genetic algorithm. The results show that the network designed in this research has a better performance in feature selection than the genetic algorithm. Vibration analysis is one of the most practical methods for monitoring and troubleshooting rotating equipment. In this research, vibration analysis and support vector machine algorithms were used for monitoring and troubleshooting Alstom locomotive blowers. First, vibration data were collected from the blowers and the received signals were categorized into four groups: healthy blowers and blowers with problems of unbalance, loose shaft (base), and warped blades. Sixteen frequency and time features were then extracted from the received signals. Because in rotating systems, the ratio of the intensity of vibrations in the harmonics of the rotation of the machine can help diagnose the faults, the ratios of all features were calculated and defined as new features. The accuracy of the network can be sometimes lowered by the multitude of features, thus, a t-test filter was inserted into the support vector machine algorithm to select the features. The results show that the t-test filter increased the accuracy of the support vector machine algorithm. Finally, the feature selection of this network was compared with the feature selection by the genetic algorithm. The results show that the network designed in this research has a better performance in feature selection than the genetic algorithm. https://mej.aut.ac.ir/article_4900_b9cfe8b6042cf759dc4c0cccb27a6737.pdf