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<p>Reliable detection of bolt loosening in safety-critical infrastructure requires monitoring that captures microsecond-scale transients. However, processing 1 MHz vibration signals at the edge presents a fundamental dilemma: standard downsampling can smear sparse diagnostic impulses, whereas full-bandwidth processing is often computationally prohibitive. Here we propose PGRF-Net, a physics-guided deep learning framework that reconciles transient preservation with extreme compression. PGRF-Net integrates (1) a physics-guided resampling operator for high-rate vibration signals (e.g., 150&#xa0;:&#xa0;1 compression); (2) a heterogeneous gradient–spectral tensor representation that augments time–frequency information with morphology-aware channels; and (3) an asymmetric fusion module over two representations derived from the same signal (pseudo-image representation + waveform representation). On a six-class 1 MHz PVDF vibration dataset (109,668 segments; temporal-split test = 16,131), PGRF-Net reaches a best-run clean test accuracy of <b>95.12%</b> under a block-wise temporal split. A strict file-disjoint split is also reported as a cross-scene hold-out protocol. On an independent Zenodo benchmark (3-fold file-disjoint CV, 9 runs per experiment), we evaluate the proposed pipeline together with three feature-engineering controls (Exp&#xa0;501–503). These results support a practical compression–learning pipeline for industrial monitoring where both transient fidelity and computational efficiency are required.</p>

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Physics-guided resampling and asymmetric fusion for ultra-high-frequency bolt-loosening detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Zihao Luo,
  • Xinrui Shi,
  • Kai Xie,
  • Chang Wen,
  • Sixiang Liu,
  • Wei Zhang

摘要

Reliable detection of bolt loosening in safety-critical infrastructure requires monitoring that captures microsecond-scale transients. However, processing 1 MHz vibration signals at the edge presents a fundamental dilemma: standard downsampling can smear sparse diagnostic impulses, whereas full-bandwidth processing is often computationally prohibitive. Here we propose PGRF-Net, a physics-guided deep learning framework that reconciles transient preservation with extreme compression. PGRF-Net integrates (1) a physics-guided resampling operator for high-rate vibration signals (e.g., 150 : 1 compression); (2) a heterogeneous gradient–spectral tensor representation that augments time–frequency information with morphology-aware channels; and (3) an asymmetric fusion module over two representations derived from the same signal (pseudo-image representation + waveform representation). On a six-class 1 MHz PVDF vibration dataset (109,668 segments; temporal-split test = 16,131), PGRF-Net reaches a best-run clean test accuracy of 95.12% under a block-wise temporal split. A strict file-disjoint split is also reported as a cross-scene hold-out protocol. On an independent Zenodo benchmark (3-fold file-disjoint CV, 9 runs per experiment), we evaluate the proposed pipeline together with three feature-engineering controls (Exp 501–503). These results support a practical compression–learning pipeline for industrial monitoring where both transient fidelity and computational efficiency are required.