A zoomed root-Prony technique for efficient bearing fault detection in induction motors

• New method for diagnosing bearing faults in induction motors. • Utilizes zoomed Root-Prony method for better frequency resolution. • Focuses on specific frequency intervals to lower computation time. • Identifies faults at low amplitudes, especially around fundamental and third harmonics. • Experimental tests show improved performance over traditional methods. This paper proposes a new diagnostic approach for identifying bearing faults in induction motors, which are common issues that can affect the performance and durability of these motors. Although various methods have been developed to diagnose these faults, we propose a high-resolution technique based on stator current analysis, enabling more effective detection and identification of these anomalies. This new approach leverages a variant of the Root-Prony method, designed to overcome the limitations of conventional periodogram methods, such as the inability to achieve high-frequency resolution with very short acquisition times. These limitations make it challenging to analyze the frequency components associated with bearing faults. In contrast, high-resolution spectral analysis using the Root-Prony method offers enhanced frequency resolution and the ability to detect faults even at low amplitudes. However, the method's high computation time remains a limiting factor. To address this, a zoomed Root-Prony method is proposed, allowing for faster and more precise diagnosis of bearing faults. Simulation and experimental tests are presented in this paper to validate the effectiveness of the proposed method.

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Publication Details

Journal
Results in Engineering
Published
2024-11-12
DOI
https://doi.org/10.1016/j.rineng.2024.103367
Citations
7
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
2.30
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article

A zoomed root-Prony technique for efficient bearing fault detection in induction motors

Zakaria Chedjara, Hafiz Ahmed, B. S. K. K. Ibrahim, Mohamed Benbouzid et al.
7 citations
Results in Engineering
Machine Fault Diagnosis Techniques
2.30
article

A zoomed root-Prony technique for efficient bearing fault detection in induction motors

Zakaria Chedjara, Hafiz Ahmed, B. S. K. K. Ibrahim, Mohamed Benbouzid, Chun‐Lien Su, Josep Maria Guerrero, Mohamed Abdeldjabbar Kouadria
article en
7 citations

Abstract

• New method for diagnosing bearing faults in induction motors. • Utilizes zoomed Root-Prony method for better frequency resolution. • Focuses on specific frequency intervals to lower computation time. • Identifies faults at low amplitudes, especially around fundamental and third harmonics. • Experimental tests show improved performance over traditional methods. This paper proposes a new diagnostic approach for identifying bearing faults in induction motors, which are common issues that can affect the performance and durability of these motors. Although various methods have been developed to diagnose these faults, we propose a high-resolution technique based on stator current analysis, enabling more effective detection and identification of these anomalies. This new approach leverages a variant of the Root-Prony method, designed to overcome the limitations of conventional periodogram methods, such as the inability to achieve high-frequency resolution with very short acquisition times. These limitations make it challenging to analyze the frequency components associated with bearing faults. In contrast, high-resolution spectral analysis using the Root-Prony method offers enhanced frequency resolution and the ability to detect faults even at low amplitudes. However, the method's high computation time remains a limiting factor. To address this, a zoomed Root-Prony method is proposed, allowing for faster and more precise diagnosis of bearing faults. Simulation and experimental tests are presented in this paper to validate the effectiveness of the proposed method.

Results in EngineeringVol. 24
Centre National de la Recherche Scientifique (FR), Université de Bretagne Occidentale (FR), Gulf University for Science & Technology (KW), University of Derby (GB), Université IBN Khaldoun Tiaret (DZ), Institut de Recherche Dupuy de Lôme (FR), National Kaohsiung University of Science and Technology (TW), University of Sheffield (GB)
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Openalex Percentile: Top 11%
Machine Fault Diagnosis Techniques
2.30
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