Load influence on lissajous figure for online transformer winding diagnosis

Liao, Yu X.; Zhu, Tian Y.; Sun, Yi; Zhang, Jie; Cheng, T.C.; Wang, Yong

doi:10.1109/ichve.2016.7800620

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…When evaluating a rotor defect, the operator often needs additional information, e.g., a view of the defect type. Therefore, we decided to use a technique known, e.g., in ovality or eccentricity measurements [34][35][36][37].…”

Section: Processing Of the Sensor Signals And Analysismentioning

confidence: 99%

Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors

2021

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In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline measurement procedures and technologies are finally validated by testing measurements on a rotor that simulates the most frequently occurring faults. A test system is also developed that provides the operator continuous information about the running rotor measurements and makes it easier to evaluate the quality of the cast rotor by means of graphical visualization of the faults.

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Section: Processing Of the Sensor Signals And Analysismentioning

confidence: 99%