Fault Diagnosis Method of Motor Bearing Based on Bayesian Optimized Quadratic Discriminant Analysis

Abstract: Motor bearing is an important part of the motor, and timely troubleshooting of motor bearing faults is of great significance to the safe and stable operation of the motor. This paper proposes a fault diagnosis method for motor bearings based on Bayesian optimized quadratic discriminant analysis (QDA). This method takes the QDA model as the main diagnostic model, with gaussian processes as the probabilistic surrogate model and expected improvement function as the collection function. The hyperparameter set of the model is optimized by using Bayesian optimization (BO). In addition, the diagnosis results of the support vector machine (SVM) and k-nearest neighbor (KNN) models are compared with QDA based on the same data set. The experimental results show that: Bayesian optimized QDA has a better performance.