Fault Monitoring and Diagnosis of Motor Operation Status Based on LBP-SVM

As an important power device in modern industry, electric motor plays a vital role in the production process. In the face of the expanding scale of the motor system and the increasing complexity of the working environment, the study proposes a new model for motor fault diagnosis based on local binary patterns and support vector machines. By analyzing the motor operation data, while extracting texture features using local binary patterns, and accurately classifying and diagnosing by support vector machines, the study aims to improve the accuracy and real-time performance of fault detection. Experimental results show that the diagnostic model is able to complete the feature classification in as little as 200 seconds, and the fault classification accuracy can reach up to 97%. The model’s fault prediction has the smallest mean square error of 0.017, the smallest root mean square error of 0.214, and the smallest mean absolute error of 0.011. From the above data, it can be seen that the proposed method of the study can significantly improve the efficiency and accuracy of fault detection. The contribution of the study is to propose an effective method for motor fault monitoring and diagnosis, which provides important support for motor fault prevention and maintenance, and also lays a new theoretical foundation for the development of motor fault diagnosis technology.