Working PaperArticleVersion 1This version is not peer-reviewed
Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods
Version 1
: Received: 17 March 2021 / Approved: 18 March 2021 / Online: 18 March 2021 (11:01:43 CET)
How to cite:
Attar, A. Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods. Preprints2021, 2021030475
Attar, A. Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods. Preprints 2021, 2021030475
Attar, A. Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods. Preprints2021, 2021030475
APA Style
Attar, A. (2021). Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods. Preprints. https://doi.org/
Chicago/Turabian Style
Attar, A. 2021 "Introducing a New Comprehensive Model for Fault Detection and Condition Monitoring of Rotor Bars of Large Induction Motors Based on MCSA and ZCT Methods" Preprints. https://doi.org/
Abstract
In this research, a model is presented to detect and monitor the rotor bar’s condition of large motors. This proposed model uses two diagnostic methods MCSA and ZCT, to extract the fault components. The input of the proposed model is only the motor current at two levels of 80% and 100% of the nominal motor load, which by using the two methods MCSA and ZCT and making changes in how to use them can be the disadvantages of other methods such as incorrect detection of rotor bars in Large motors with variable load, the harmonical stator voltage (or the presence of the drives) and asymmetric conditions. The extracted components are classified using neural network, instead of experimental and human resources constraints. Using this classification algorithm is high accuracy and the ability to implement in conventional processors. The proposed model has been trained andtested using currents of real large motor data and 96.87% accuracy using neural network to detect faults and monitor the online status of the rotor bars. On the other hand, the proposed model has been implemented in the industrial environment (FiroozkoohCement Factory) and has been able to correctly determine the rotor bars of 4 large suspicious motors announced by the factory technicians.
Keywords
condition monitoring; faulty rotor bar detection; large induction motor; neural network; classification; MCSA; ZCT
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
