SELF LEARNING FAULT DETECTION FOR ELECTRICAL MOTORS

原文（中国語）

Some embodiments relate to a method and system for determining electrical motor fault comprising: measuring ambient vibration data of the electrical motor and performing spectral processing of the ambient vibration data; filtering noise data from the ambient vibration data, outputting a filtered ground truth vibration spectrum, clustering features of the filtered ground truth vibration spectrum, and determining temporal and spatial distance between the clustered features; measuring electrical data signals of the electrical motor, analysing the electrical data signals to extract features of the electrical data signals and storing the extracted features from the electrical data signal; assigning an operating state label to the ground truth vibration spectrum by comparing the clustered features of the ground truth operational vibration spectrum to a database of stored training data and ground truth algorithms; determining an accuracy of the operating state label; and determining electrical motor fault using a new measured electrical data signal and the stored ground truth algorithm to determine if the electrical motor is in a fault state.