Open AccessRapid evaluation of the mechanical fault severity in induction motors using the model‐based diagnosis technique
Author(s) -
Huang Jiajie,
Liu Yongqiang,
Liang Zhaowen
Publication year - 2021
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/elp2.12012
Subject(s) - stator , induction motor , fault (geology) , vibration , amplitude , control theory (sociology) , component (thermodynamics) , rotor (electric) , mechanical system , line (geometry) , engineering , current (fluid) , condition monitoring , control engineering , computer science , acoustics , mathematics , physics , mechanical engineering , artificial intelligence , electrical engineering , voltage , geometry , control (management) , quantum mechanics , seismology , geology , thermodynamics
The motor current signal analysis (MCSA) technique is widely used as a non‐invasive method for detecting mechanical faults in induction motors by capturing characteristic components in the stator line current. However, the threshold of the characteristic component is not clear now, which makes it difficult for MCSA to judge whether the fault occurs or evaluates the mechanical fault severity. The existing model‐based evaluation methods cannot meet the requirement of online condition monitoring because of their slow calculation. To solve these problems, a simplified dynamic motor model under any type of mechanical fault is established, and a formula for the amplitude relationship between the radial vibration of the rotor and fault‐related component in the stator line current is derived. The radial vibration amplitude is related to mechanical fault severity. Using this formula, the MCSA technique can rapidly evaluate the mechanical fault severity according to the amplitude of the characteristic component in the collected stator current. The simulation study results demonstrate the accuracy of the simplified model and formula. The experimental results presented for condition monitoring in a real induction motor clearly validate the evaluation approach.