Bearing various defects diagnosis and classification using super victor machine (SVM) method

Today, the diagnosis and classification of faults is very important, especially in rotating machines to avoid damage to material and human losses. This paper focuses on the use of artificial intelligence methods and specifically the Super Victor Machine (SVM) for bearing fault classification. Many methods and techniques have been used in this field to detect and predict bearing faults, but none of them is perfect. Most of these methods have many drawbacks, such as the complexity of extracting vibration signals. The present work uses Support Vector Machines (SVM) to overcome all issues of previously used methods for better accuracy. All the results show the effectiveness of the proposed technique over the previous method and can be employed as an effective tool in the classification of induction motor faults.