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<p>Accurate detection of defects in steel ropes is a crucial prerequisite for monitoring their health status. However, the detection process is often affected by noise interference, resulting in a low signal-to-noise ratio (<i>SNR</i>) and making it difficult to detect small wire break defects. To address this issue, this study proposes a signal processing method for steel rope defect detection that effectively enhances the detection capability of small wire break defects. First, based on the spatial response characteristics between the defect and the sensor array, a strategy using relative peaks and waveform slopes is proposed to eliminate invalid channels containing strand and shaking noise, thereby improving the input signal quality before subsequent denoising. Second, a cascaded workflow integrating multi-stage filtering and nonlinear mapping is constructed to achieve seamless noise stripping and feature enhancement. Kalman filtering and dual median filtering are used for noise reduction to suppress strand and shaking noise, while gamma transformation enhances defect signal features and wavelet denoising extracts the optimized characteristics, facilitating the identification of small defect signals. The experimental results show that, compared with the six reported methods, the method achieves a relatively high <i>SNR</i> of 12.18&#xa0;dB, the highest peak fidelity of 95.96%, and a minimal miss rate of 5.00%. These improvements effectively enhance the detection capability of small wire break defects.</p>

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Inspection of Small Damage Signals for Evaluation of Steel Wire Ropes Under Strong Noises

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Guang Li,
  • Lin Yang,
  • Yifan Jiang,
  • Mengbao Fan,
  • Fengshan Sun,
  • Binghua Cao

摘要

Accurate detection of defects in steel ropes is a crucial prerequisite for monitoring their health status. However, the detection process is often affected by noise interference, resulting in a low signal-to-noise ratio (SNR) and making it difficult to detect small wire break defects. To address this issue, this study proposes a signal processing method for steel rope defect detection that effectively enhances the detection capability of small wire break defects. First, based on the spatial response characteristics between the defect and the sensor array, a strategy using relative peaks and waveform slopes is proposed to eliminate invalid channels containing strand and shaking noise, thereby improving the input signal quality before subsequent denoising. Second, a cascaded workflow integrating multi-stage filtering and nonlinear mapping is constructed to achieve seamless noise stripping and feature enhancement. Kalman filtering and dual median filtering are used for noise reduction to suppress strand and shaking noise, while gamma transformation enhances defect signal features and wavelet denoising extracts the optimized characteristics, facilitating the identification of small defect signals. The experimental results show that, compared with the six reported methods, the method achieves a relatively high SNR of 12.18 dB, the highest peak fidelity of 95.96%, and a minimal miss rate of 5.00%. These improvements effectively enhance the detection capability of small wire break defects.