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

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.