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A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed

Ben Sasi, Ahmed Y., Gu, Fengshou, Li, Yuhua ORCID: and Ball, Andrew D. 2006. A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed. Mechanical Systems and Signal Processing 20 (7) , pp. 1572-1589. 10.1016/j.ymssp.2005.09.010

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Instantaneous angular speed (IAS)-based condition monitoring is an area in which significant progress has been achieved over the recent years. This condition monitoring technique is less known compared to the existing conventional methods. This paper presents model-predicted simulation and experimental results of broken rotor bar faults in a three-phase induction motor using IAS variations. The simulation was performed under normal, and a broken rotor bar fault. The present paper evaluates through simulating and measuring the IAS of an induction motor at broken rotor bar faults in both time and frequency domains. Experimental results show a good agreement with the model-predicted simulation results. Three vital key features were extracted from the angular speed variations. One feature is the modulating contour of pole pass frequency periods in time domain. The other two features are in frequency domain. The primary feature is the presence of the pole pass frequency component at the low-frequency region of the IAS spectrum. The secondary feature which are the multiple of pole pass frequency sideband components around the rotor speed frequency component. Experimental results confirm the validity of the simulation results for the proposed method. The IAS has demonstrated more sensitivity than current signature analysis in detecting the fault. This research also shows the power of angular speed features as a useful tool to detect broken rotor bar deteriorations using any economical transducer such as low-resolution rotary shaft encoders; which may well be already installed for process control purposes.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0888-3270
Date of Acceptance: 15 September 2015
Last Modified: 07 Nov 2022 09:26

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