Modelling, Analysis And Control of Five Phase Induction Motor Drive under Open Circuit Fault for Electric Vehicle

Gor, Chandani; Shah, Varsha A.

doi:10.1109/icesip46348.2019.8938312

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…identifying and diagnosing faults in IMs before they lead to motor failure, and include techniques such as vibration analysis, Motor Current Signal Analysis (MCSA), and motor current signature analysis [8,[10][11][12][13][14][15][16][17]. By implementing these fault diagnosis methods, the reliability of IMs can be enhanced, thereby improving the overall performance and safety of EVs.…”

Section: Of 17mentioning

confidence: 99%

Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor

Behnamnia¹,

Mardaneh²,

Jamshidpour³

2023

Preprint

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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.

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