Bayes' Theorem for Multi-Bearing Faults Diagnosis

Abstract

During the process of fault diagnosis for automated machinery, support vector machines is one of the suitable choices to categorize multiple faults for machinery. Regardless of the volume of sampling data, support vector machines can handle a high number of input features. It was learned that support vector machines could only sense binary fault classification (such as faulty or healthy). However, the classification accuracy was found to be lower when using support vector machines to diagnose multi-bearing faults classifications. This is because the multiple classification problem will be reduced into several sub-problems of binary classification when support vector machines adapt to multi-bearing faults classifications. From there, many contradictory results will occur from every support vector machine model. In order to solve the situation, the combination of Support Vector Machines and Bayes’ Theorem is introduced to every single support vector machine model to overcome the conflicting results. This method will also increase classification accuracy. The proposed Support Vector Machines - Bayes’ Theorem method has resulted in an increase in the accuracy of the fault diagnosis model. The analysis result has shown an accuracy from 72% to 95%. It proved that Support Vector Machines - Bayes’ Theorem continuously eliminates and refines conflicting results from the original support vector machine model. Compared to the existing support vector machine, the proposed Support Vector Machines - Bayes’ Theorem has proven its effectiveness in diagnosing the multi-bearing faults problem classification.