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Purpose <p>Gear faults are difficult to diagnose using vibration signals in a noisy environment.</p> Method <p>This paper proposes a novel approach for detecting gear faults amidst noisy vibration signals by integrating Minimum Entropy Deconvolution (MED) for initial signal denoising with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition into Intrinsic Mode Functions (IMFs). The selection of relevant IMFs employs Approximate Entropy (ApEn), Dynamic Time Warping (DTW), and Fault Correlation Factor (FCF). Unlike existing denoising methods that compromise information for higher Signal-to-Noise Ratio (SNR), the proposed methodology effectively preserves gear health information during denoising.</p> Results <p>Notably, this approach surpasses current algorithms in detecting worn and chipped tooth faults in gears based on modulation-based models. Validation encompasses two distinct gear datasets from single-stage and multi-stage gearboxes operating under various conditions, such as different speeds and loads.</p>

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A Novel Methodology for Chipped Teeth and Worn Tooth Gear Fault Detection Using Minimum Entropy Deconvolution and CEEMDAN

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Priyom Goswami,
  • Rajiv Nandan Rai

摘要

Purpose

Gear faults are difficult to diagnose using vibration signals in a noisy environment.

Method

This paper proposes a novel approach for detecting gear faults amidst noisy vibration signals by integrating Minimum Entropy Deconvolution (MED) for initial signal denoising with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition into Intrinsic Mode Functions (IMFs). The selection of relevant IMFs employs Approximate Entropy (ApEn), Dynamic Time Warping (DTW), and Fault Correlation Factor (FCF). Unlike existing denoising methods that compromise information for higher Signal-to-Noise Ratio (SNR), the proposed methodology effectively preserves gear health information during denoising.

Results

Notably, this approach surpasses current algorithms in detecting worn and chipped tooth faults in gears based on modulation-based models. Validation encompasses two distinct gear datasets from single-stage and multi-stage gearboxes operating under various conditions, such as different speeds and loads.