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

Idrissi, Abderrahman El; Derouich, Aziz; Mahfoud, Said; Ouanjli, Najib El; Chantoufi, Ahmed

doi:10.1007/978-3-031-29860-8_59

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Induction Motors (IMs) are commonly employed in various industrial applications due to their cost-effectiveness and robustness [1], [2]. In pursuit of heightened production, it becomes imperative for asynchronous motors to operate continuously without interruptions [3].…”

Section: Introductionmentioning

confidence: 99%

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Idrissi,

Derouich,

Mahfoud

et al. 2024

IEEE Access

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Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM's overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

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Section: Introductionmentioning

confidence: 99%

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Idrissi,

Derouich,

Mahfoud

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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

Cited by 2 publications

References 16 publications

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

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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