Fault Diagnosis of the Bearing Outer Ring of an Induction Motor Under DTC Control by Using Hilbert Filter

Idrissi, Abderrahman El; Derouich, Aziz; Mahfoud, Said

doi:10.1007/978-3-031-01942-5_80

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…According to our investigation and extensive literature evaluation, signal processing is among the most important methodologies used in fault diagnosis. e signal processing approach may be separated into two halves, the first of which uses traditional techniques such as FFT [20] and Hilbert analysis [21], and the second of which uses novel techniques such as windowed Fourier transforms and wavelet analysis [22]. Frequency examination of quantifiable variables is currently the important method utilized approach for defect analysis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

R¹,

Irudayaraj

Devarajan³

et al. 2022

International Transactions on Electrical Energy Systems

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The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

R¹,

Irudayaraj

Devarajan³

et al. 2022

International Transactions on Electrical Energy Systems

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Bearing Ball Fault Diagnosis of an Induction Machine by Using the Hilbert Transform and the Performance of Intelligent Control

Idrissi

Derouich

Mahfoud

et al. 2023

Lecture Notes in Networks and Systems

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Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

El Idrissi,

Derouich,

Mahfoud

et al. 2024

Electronics

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Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.

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Fault Diagnosis of the Bearing Outer Ring of an Induction Motor Under DTC Control by Using Hilbert Filter

Cited by 5 publications

References 9 publications

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

Bearing Ball Fault Diagnosis of an Induction Machine by Using the Hilbert Transform and the Performance of Intelligent Control

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

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