Application of Distance Measure in KNN Motor Fault Diagnosis

Abstract

Motor bearings play an important role in industrial production. Regular inspection and fault diagnosis can provide reliable guarantee for the normal operation of the motor. If the potential or early bearing failures are not detected in time, they will bring safety hazards to production operations. In this thesis, distance measure method for feature extraction and KNN (k-nearest neighbor) algorithm for recognition are combined for the purpose of diagnosis of motor bearing. Through experimental verification, the medium KNN algorithm has the most obvious improvement, and the accuracy has increased from 39. 1% to 99. 8% with the help of the proposed feature extraction. Using the KNN algorithm, the effect of time domain feature extraction, frequency domain feature extraction and distance feature extraction are verified. After comparison, it is concluded that the distance feature extraction method can effectively improve the accuracy rate and speed up the training speed of the model.