YH
Y. Han
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Recent advances in quantum photonics and single-photon detection are enabling new sensing capabilities for vibration-based diagnostics and non-destructive evaluation. This work investigates the signal characteristics and effective resolution of the Quantum Photonic Vibrometer (QPV), a quantum-enhanced, non-contact sensor designed for sub-nanometric vibration measurements in Structural Health Monitoring (SHM). The QPV operates at a telecom wavelength of 1550 nm using a mode-locked laser emitting 20 MHz optical pulses, combined with time-gated photon detection on an avalanche photodiode (APD). The measured photon-count signal arises from phase-induced speckle modulation, where structural vibrations modulate the optical phase of the backscattered field, producing measurable intensity fluctuations. In this study, photon-count time series obtained from aluminum specimens (pure and with artificial defects) are analyzed using time- and frequency-domain techniques, including Fast Fourier Transform (FFT), sliding Root Mean Square (RMS), variance, and Hilbert envelope extraction. A simplified physical model is introduced to relate photon-count fluctuations to vibration-induced phase variations, providing insight into the effective measurement resolution in the presence of photon shot noise. Results show that pure aluminum exhibits stable and low-variance responses with consistent spectral signatures, while defective samples introduce measurable changes in frequency content and signal variability. These observations indicate that the QPV signal contains physically meaningful information related to structural condition, and that its sensitivity is governed by the interplay between speckle-induced phase modulation and photon-count statistics. This work highlights the potential of QPV as a sensing modality for vibration analysis and defect-sensitive diagnostics, establishing a foundation for data-driven structural health monitoring using quantum-enhanced vibrometry.
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Recent advances in quantum photonics and single-photon detection are enabling new sensing capabilities for vibration-based diagnostics and non-destructive evaluation. This work investigates the signal characteristics and effective resolution of the Quantum Photonic Vibrometer (QPV), a quantum-enhanced, non-contact sensor designed for sub-nanometric vibration measurements in Structural Health Monitoring (SHM). The QPV operates at a telecom wavelength of 1550 nm using a mode-locked laser emitting 20 MHz optical pulses, combined with time-gated photon detection on an avalanche photodiode (APD). The measured photon-count signal arises from phase-induced speckle modulation, where structural vibrations modulate the optical phase of the backscattered field, producing measurable intensity fluctuations. In this study, photon-count time series obtained from aluminum specimens (pure and with artificial defects) are analyzed using time- and frequency-domain techniques, including Fast Fourier Transform (FFT), sliding Root Mean Square (RMS), variance, and Hilbert envelope extraction. A simplified physical model is introduced to relate photon-count fluctuations to vibration-induced phase variations, providing insight into the effective measurement resolution in the presence of photon shot noise. Results show that pure aluminum exhibits stable and low-variance responses with consistent spectral signatures, while defective samples introduce measurable changes in frequency content and signal variability. These observations indicate that the QPV signal contains physically meaningful information related to structural condition, and that its sensitivity is governed by the interplay between speckle-induced phase modulation and photon-count statistics. This work highlights the potential of QPV as a sensing modality for vibration analysis and defect-sensitive diagnostics, establishing a foundation for data-driven structural health monitoring using quantum-enhanced vibrometry.
For structural dynamics and health monitoring, vision-based vibration measurement has become an attractive approach thanks to its full-field, non-contact, and cost-effective aspect. However, conventional frame-based cameras are inherently constrained by frame rate, motion blur, and exposure duration, while producing vast amounts of redundant data. These limitations restrict their ability to capture high-frequency vibrations over extended periods or under low illumination. In contrast, event cameras asynchronously record pixel-level brightness changes with microsecond latency and a high dynamic range. This enables high-frequency motion tracking under ambient light or uneven light condition and ensures efficient data use, as events are generated only where motion occurs. This study presents a unified event-based framework for estimating vibration and modal parameters within a dynamic state-space formulation. Displacement information is extracted from images reconstructed from event streams, while velocity information is obtained directly from the events. By combining these complementary motion signals with a physics-based vibration model via data assimilation, the method enables accurate and continuous estimation of structural vibrations with strong robustness to noise. Experiments were performed on a cantilever beam with an event camera and a reference accelerometer synchronized in operation, including both free and forced vibration tests. In the free-vibration tests, modal parameters of the first mode were identified from the decaying response, whereas the forced-vibration setup used a shaker to apply harmonic excitation, enabling the estimation of the operational deflection shapes under steady-state conditions. The extracted modal properties closely matched the accelerometer measurements, validating the proposed approach. The proposed framework demonstrates the capability of the event-camera-based approach to measure structural vibrations with high temporal fidelity and strong robustness to noise. It combines event data with a physics-based vibration model through data assimilation. This integration enables accurate modal identification and efficient motion estimation without the motion blur, saturation, or illumination constraints inherent to frame-based systems. These findings highlight the potential of event cameras as a robust and data-efficient sensing modality for continuous, high-frequency monitoring in structural dynamics and health assessment.
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For structural dynamics and health monitoring, vision-based vibration measurement has become an attractive approach thanks to its full-field, non-contact, and cost-effective aspect. However, conventional frame-based cameras are inherently constrained by frame rate, motion blur, and exposure duration, while producing vast amounts of redundant data. These limitations restrict their ability to capture high-frequency vibrations over extended periods or under low illumination. In contrast, event cameras asynchronously record pixel-level brightness changes with microsecond latency and a high dynamic range. This enables high-frequency motion tracking under ambient light or uneven light condition and ensures efficient data use, as events are generated only where motion occurs. This study presents a unified event-based framework for estimating vibration and modal parameters within a dynamic state-space formulation. Displacement information is extracted from images reconstructed from event streams, while velocity information is obtained directly from the events. By combining these complementary motion signals with a physics-based vibration model via data assimilation, the method enables accurate and continuous estimation of structural vibrations with strong robustness to noise. Experiments were performed on a cantilever beam with an event camera and a reference accelerometer synchronized in operation, including both free and forced vibration tests. In the free-vibration tests, modal parameters of the first mode were identified from the decaying response, whereas the forced-vibration setup used a shaker to apply harmonic excitation, enabling the estimation of the operational deflection shapes under steady-state conditions. The extracted modal properties closely matched the accelerometer measurements, validating the proposed approach. The proposed framework demonstrates the capability of the event-camera-based approach to measure structural vibrations with high temporal fidelity and strong robustness to noise. It combines event data with a physics-based vibration model through data assimilation. This integration enables accurate modal identification and efficient motion estimation without the motion blur, saturation, or illumination constraints inherent to frame-based systems. These findings highlight the potential of event cameras as a robust and data-efficient sensing modality for continuous, high-frequency monitoring in structural dynamics and health assessment.