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<p>Pipeline structural health monitoring is critical for global energy security, yet traditional bulk piezoelectric acoustic emission (AE) sensors are inherently bulky, narrow-band, and unsuitable for large-scale, distributed deployment. Here, we present a highly integrated, broadband microelectromechanical systems (MEMS) AE sensor based on ScAlN piezoelectric micromachined ultrasonic transducers (PMUTs) for intelligent pipeline damage monitoring. To address the acoustic impedance mismatch between the silicon-based micro-chip and external media, a composite acoustic matching layer comprising epoxy resin doped with 60 wt% Al<sub>2</sub>O<sub>3</sub> powder was engineered. This packaging strategy significantly enhances acoustic transmission efficiency while ensuring exceptional mechanical robustness in harsh environments. Systematic characterizations reveal that the fabricated MEMS AE sensor exhibits a broad bandwidth with displacement sensitivity exceeding 60 dB across 40–600 kHz, and a peak sensitivity of 88.4 dB at 335 kHz. In standard pencil lead break tests, it demonstrates a signal amplitude approximately twice that of commercial AE sensors. Furthermore, the device maintains stable performance under severe thermal shock cycling from −55 °C to 85 °C. By integrating this hardware with a Short-Time Fourier Transform (STFT) and a Residual Neural Network (ResNet)-based deep learning algorithm, we developed an intelligent pipeline monitoring system. The system successfully captured and classified the time-frequency characteristics of five distinct human-induced destructive behaviors with an outstanding recognition accuracy of 100%. This work provides a scalable, high-performance hardware-software paradigm for distributed structural health monitoring in extreme industrial environments.</p>

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Intelligent monitoring system for pipeline status based on MEMS acoustic emission sensors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Tao Liu,
  • Wenwu Yan,
  • Dongxiao Li,
  • Jidong Cai,
  • Jian Feng,
  • Hanjie Dou,
  • Jiaqian Yang,
  • Pengfan Wu,
  • Hongliang Wang,
  • Xiaojing Mu

摘要

Pipeline structural health monitoring is critical for global energy security, yet traditional bulk piezoelectric acoustic emission (AE) sensors are inherently bulky, narrow-band, and unsuitable for large-scale, distributed deployment. Here, we present a highly integrated, broadband microelectromechanical systems (MEMS) AE sensor based on ScAlN piezoelectric micromachined ultrasonic transducers (PMUTs) for intelligent pipeline damage monitoring. To address the acoustic impedance mismatch between the silicon-based micro-chip and external media, a composite acoustic matching layer comprising epoxy resin doped with 60 wt% Al2O3 powder was engineered. This packaging strategy significantly enhances acoustic transmission efficiency while ensuring exceptional mechanical robustness in harsh environments. Systematic characterizations reveal that the fabricated MEMS AE sensor exhibits a broad bandwidth with displacement sensitivity exceeding 60 dB across 40–600 kHz, and a peak sensitivity of 88.4 dB at 335 kHz. In standard pencil lead break tests, it demonstrates a signal amplitude approximately twice that of commercial AE sensors. Furthermore, the device maintains stable performance under severe thermal shock cycling from −55 °C to 85 °C. By integrating this hardware with a Short-Time Fourier Transform (STFT) and a Residual Neural Network (ResNet)-based deep learning algorithm, we developed an intelligent pipeline monitoring system. The system successfully captured and classified the time-frequency characteristics of five distinct human-induced destructive behaviors with an outstanding recognition accuracy of 100%. This work provides a scalable, high-performance hardware-software paradigm for distributed structural health monitoring in extreme industrial environments.