Diagnosing Stator And Rotor Fault Of Induction Motor Based-on The Analysis Of The Stator Current

With the rapid development of modern society, three-phase induction motors arewidely used in various fields of the industrial and agricultural production, because of itssimple structure, easy maintenance, low cost, high reliability. The detection of the motorrunning status could discover faults in the early stage and prevent the deterioration toensure the normal production of agriculture and industry. Therefore, the research has greateconomic significance. Based on this background, this paper did the research on the statorand rotor fault diagnosis, which is based on the analysis of the stator current.Firstly, the motor fault characteristic of inter-turn short circuit, broken rotor bar andmotor eccentricity is deduced from the perspective of electromagnetic theory. Secondly,when inter-turn short circuit faults happen in stator, the traditional method of symmetriccomponent has lost the application premise because of the harmonics and noise in statorcurrents, the negative sequential component seen as the characteristic of inter-turn shortcircuit fault is hard to be obtained accurately. To solve this problem, this paper presents theimproved correlation algorithm which can extract the fundamental component from thestator current accurately. Via the anti-synchronous-speed coordinate transformation, thenegative sequence one is transformed into the DC component. The sensitivity factor of thecharacteristic of inter-turn short circuit fault is calculated from the negative sequencecomponent in the anti-synchronous-speed coordinate transformation. In case of brokenrotor bar and motor eccentricity, the fault characteristic is blanketed by fundamental one inthe spectrum of stator current. To solve the problem, this paper presents the improvedcorrelation algorithm which can filter out the fundamental component to make thecharacteristic of edge-frequency components emerge from a spectrum of stator current.And then, the algorithm is simulated by Matlab. The32-bit microprocessor-S3C2440A ischosen as the core of motor parameter acquisition, the Labview2010is chosen as theplatform of data analysis and fault diagnosis. A set of motor fault diagnosis system basedon the improved correlation algorithm is constructed successfully. Finally, the experimentresults show that the previous design idea is feasible in different experimental conditions.