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<p>Ensuring the reliability and efficiency of brushless direct current (BLDC) motors in electric vehicles (EVs) requires early fault detection to prevent unexpected failures and costly maintenance. Mechanical and electrical faults are the primary causes of motor degradation, often leading to downtime and reduced performance. This research introduces a cost-effective, non-invasive diagnostic approach for detecting dynamic eccentricity faults in BLDC motors, addressing the limitations of existing methods. The proposed technique employs ultra-wideband (UWB) radar to emit high-frequency signals toward the motor, capturing reflected signals for analysis using SIGVIEW software. A software phase-locked loop (SPLL) functions as a low-pass filter to eliminate high-frequency noise, while rational dilation wavelet transforms (RDWT) extract fault-relevant features from the processed signals. The RDWT output is analyzed in MATLAB-R2022b, revealing significant energy variations in the level-6 sub-band corresponding to different dynamic eccentricity levels: a 23.13% increase at 10%, 35.69% at 20%, and 40.87% at 30%. By enabling early fault detection, this method facilitates proactive maintenance strategies, reducing the risk of unexpected failures and extending the operational lifespan of BLDC motors in EVs. The integration of advanced signal processing techniques enhances fault diagnosis accuracy while maintaining a cost-effective and sustainable approach. This innovative methodology contributes to the development of more reliable and efficient electric vehicle systems, supporting the advancement of sustainable transportation technologies.</p>

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Cost-effective non-invasive vibration analysis for detecting eccentricity faults in BLDC motors using microwave radar-based software PLL and RDWT

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • P. Raja Shekhar,
  • G. Sumithra,
  • D. Meganathan

摘要

Ensuring the reliability and efficiency of brushless direct current (BLDC) motors in electric vehicles (EVs) requires early fault detection to prevent unexpected failures and costly maintenance. Mechanical and electrical faults are the primary causes of motor degradation, often leading to downtime and reduced performance. This research introduces a cost-effective, non-invasive diagnostic approach for detecting dynamic eccentricity faults in BLDC motors, addressing the limitations of existing methods. The proposed technique employs ultra-wideband (UWB) radar to emit high-frequency signals toward the motor, capturing reflected signals for analysis using SIGVIEW software. A software phase-locked loop (SPLL) functions as a low-pass filter to eliminate high-frequency noise, while rational dilation wavelet transforms (RDWT) extract fault-relevant features from the processed signals. The RDWT output is analyzed in MATLAB-R2022b, revealing significant energy variations in the level-6 sub-band corresponding to different dynamic eccentricity levels: a 23.13% increase at 10%, 35.69% at 20%, and 40.87% at 30%. By enabling early fault detection, this method facilitates proactive maintenance strategies, reducing the risk of unexpected failures and extending the operational lifespan of BLDC motors in EVs. The integration of advanced signal processing techniques enhances fault diagnosis accuracy while maintaining a cost-effective and sustainable approach. This innovative methodology contributes to the development of more reliable and efficient electric vehicle systems, supporting the advancement of sustainable transportation technologies.