Current- and Vibration-Based Detection of Misalignment Faults in Synchronous Reluctance Motors

Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques rely on vibration monitoring, which provides insights into both mechanical and electromagnetic fault signatures. However, its main drawback is the need for external sensors, which may not be feasible in certain applications. Alternatively, motor current signature analysis (MCSA) has proven effective in detecting faults without requiring additional sensors. This study investigates misalignment faults in synchronous reluctance motors (SynRMs) by analyzing both vibration and current signals under different load conditions and operating speeds. Fast Fourier transform (FFT) is applied to extract characteristic frequency components linked to misalignment. Experimental results reveal that the amplitudes of rotational frequency harmonics (1xfr, 2xfr, and 3xfr) increase in the presence of misalignment, with 1xfr exhibiting the most stable progression. Additionally, acceleration-based vibration analysis proves to be a more reliable diagnostic tool compared to velocity measurements. These findings highlight the potential of combining current and vibration analysis to enhance misalignment detection in SynRMs, improving predictive maintenance strategies in industrial applications.

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Publication Details

Journal
Machines
Published
2025-04-14
DOI
https://doi.org/10.3390/machines13040319
Citations
3
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
3.64
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article

Current- and Vibration-Based Detection of Misalignment Faults in Synchronous Reluctance Motors

Vicente Biot-Monterde, José E. Ruiz-Sarrió, Jose A. Antonino‐Daviu, Ángela Navarro-Navarro
3 citations
Machines
Machine Fault Diagnosis Techniques
3.64
article

Current- and Vibration-Based Detection of Misalignment Faults in Synchronous Reluctance Motors

Vicente Biot-Monterde, José E. Ruiz-Sarrió, Jose A. Antonino‐Daviu, Ángela Navarro-Navarro
article en
3 citations

Abstract

Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques rely on vibration monitoring, which provides insights into both mechanical and electromagnetic fault signatures. However, its main drawback is the need for external sensors, which may not be feasible in certain applications. Alternatively, motor current signature analysis (MCSA) has proven effective in detecting faults without requiring additional sensors. This study investigates misalignment faults in synchronous reluctance motors (SynRMs) by analyzing both vibration and current signals under different load conditions and operating speeds. Fast Fourier transform (FFT) is applied to extract characteristic frequency components linked to misalignment. Experimental results reveal that the amplitudes of rotational frequency harmonics (1xfr, 2xfr, and 3xfr) increase in the presence of misalignment, with 1xfr exhibiting the most stable progression. Additionally, acceleration-based vibration analysis proves to be a more reliable diagnostic tool compared to velocity measurements. These findings highlight the potential of combining current and vibration analysis to enhance misalignment detection in SynRMs, improving predictive maintenance strategies in industrial applications.

MachinesVol. 13(4)
Instituto Tecnológico de la Energía (ES), Universitat Politècnica de València (ES)
Openalex Percentile: Top 8%
Machine Fault Diagnosis Techniques
3.64
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Current- and Vibration-Based Detection of Misalignment Faults in Synchronous Reluctance Motors — Vicente Biot-Monterde, José E. Ruiz-Sarrió, et al. · Machines (2025) | TGRS Research Map | TGRS