Asynchronous motor fault monitoring and diagnosing method based on deep learning

Abstract

The invention discloses an asynchronous motor fault monitoring and diagnosing method based on deep learning. The asynchronous motor fault monitoring and diagnosing method comprises the following stepsthat electric power load time series of an asynchronous motor in known working condition types are acquired, the time span is Num1 electric power load cycles, and electric power load data at each sample time includes data of three dimensions of voltage, current and power; the voltage data, the current data and the power data are separately used as the gray value of pixel points of three layers inRGB images, and the time series of the electric power load cycles are transformed into the RGB image in a segmented mode, and each electric power load time series correspondingly obtains a set of feature image time series; and a deep neural network is trained by the feature image time series of the asynchronous motor and the corresponding working condition types, and a fault diagnosis model is obtained and then used for classifying the working conditions of the asynchronous motor to be tested. The fault diagnostic accuracy of the asynchronous motor fault monitoring and diagnosing method is high, and the threshold of employees is reduced while saving the time of system development.

Description

technical field [0001] The invention relates to the field of motor fault diagnosis, and specifically refers to a method for monitoring and diagnosing asynchronous motor faults based on deep learning. Background technique [0002] The motor is the link and bridge for the mutual conversion of electrical energy and mechanical energy. The most widely used motor is the asynchronous motor, which plays a very important role in scientific research and daily production and life. As power equipment, asynchronous motors play a very important role in industrial production. During the operation of equipment, the failure of asynchronous motors will threaten the smooth progress of production activities, and even cause huge economic losses and casualties. Therefore, monitoring the running state of the motor can prevent problems before they happen, and effectively avoid the expansion of losses. [0003] Statistics show that five types of faults, including stator winding fault, broken rotor ...

AI Technical Summary

Problems solved by technology

This method needs to establish an accurate mathematical model for the motor system, the steps are cumbersome, and a large number of feature quantities need to be manually searched to ensure the accuracy of recognition.

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