Version 1
: Received: 15 May 2023 / Approved: 16 May 2023 / Online: 16 May 2023 (05:44:51 CEST)
How to cite:
Behnamnia, H.; Mardaneh, M.; Jamshidpour, E. Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor. Preprints2023, 2023051108. https://doi.org/10.20944/preprints202305.1108.v1
Behnamnia, H.; Mardaneh, M.; Jamshidpour, E. Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor. Preprints 2023, 2023051108. https://doi.org/10.20944/preprints202305.1108.v1
Behnamnia, H.; Mardaneh, M.; Jamshidpour, E. Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor. Preprints2023, 2023051108. https://doi.org/10.20944/preprints202305.1108.v1
APA Style
Behnamnia, H., Mardaneh, M., & Jamshidpour, E. (2023). Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor. Preprints. https://doi.org/10.20944/preprints202305.1108.v1
Chicago/Turabian Style
Behnamnia, H., Mohammad Mardaneh and Ehsan Jamshidpour. 2023 "Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor" Preprints. https://doi.org/10.20944/preprints202305.1108.v1
Abstract
Fault detection at the early stage of development is of great importance in the maintenance of electric motors. In this regard, Motor Current Signature Analysis (MCSA) is cited in many articles to detect rotor bar fracture. The popularity of MCSA is due to its non-invasive and non-destructive nature, simplicity, and, compatibility with several signal processing tools. The vast variety of the signal processing tools which are commonly used for detecting the broken rotor bar fault is based on the Fast Fourier Transform (FFT) of the stator current signal and analysis of the produced Fourier spectrum; however, because of the small amplitude at the fault frequency relative to the main frequency amplitude, the former cannot be easily discriminated from the latter. Therefore, the peak at the fault frequency is hidden in the shadow of the main frequency component, hence the fault will not be detectable. As a solution to vanish the base frequency component, a method based on the Algebraic Identity of trinomial expansion is proposed in this paper which enables us to display the difference between the frequency of the fault signature and the base frequency. In this article, the fault of the broken rotor bars of the squirrel cage induction motor is revealed by the proposed method. In addition, a frequency weighting technique is presented to magnify the component at the fault related frequency compared to the lower frequencies. To validate the proposed methods, they are examined on the laboratory data obtained from three different operating conditions including the direct online start, the direct torque control, and the scalar control and the results show the ability of the proposed methods in fault detection of an induction motor.
Keywords
Fault detection; Motor Current Signature Analysis (MCSA); Broken Rotor Bar (BRB); Squirrel Cage Induction Motor (SQIM); Fast Fourier Transform (FFT)
Subject
Engineering, Electrical and Electronic 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.
