QuantWear: Quantum-scale Wear Particle Detection for Jet Engine Diagnosis

The quantity and 3-D shape of wear particles are essential indicators for assessing the health of jet engines, enabling early detection of potential damage and preventing accidents caused by catastrophic failures. However, capturing wear particles is difficult due to their minute sizes and ultra high-speed movement within intense jet flows. Existing technologies struggle with the extreme background noise and low resolution in such harsh environments. In this paper, we propose QuantWear, the first quantum sensing system designed to directly quantify and profile wear particles on the sub-millimeter scale. QuantWear innovatively tracks wear particles by monitoring the spectral signatures of Sodium (Na) and Potassium (K) atoms within jet flow, which naturally adhere to particle surfaces due to electrochemical reactions in high-temperature combustion. We construct a custom atomic detector that leverages quantum jump and Faraday rotation effects to isolate these specific atomic signals, effectively suppressing the broad-spectrum flame noise. Next, we apply a deep learning framework to effectively measure the quantity of wear particles in dynamic vaporous backgrounds. Finally, we generate a fully reconstructed 3-D model of the wear particles from multiple 2-D images. Extensive field tests and high-fidelity simulations demonstrate that QuantWear achieves an imaging Signal-to-Noise Ratio (SNR) of 22.5 dB and a 3-D reconstruction similarity exceeding 95%, significantly outperforming state-of-the-art technologies.

Tiny metal pieces, known as wear particles, break off inside jet engines and act as crucial early warning signs of potential engine failure. However, detecting and measuring these sub-millimetre particles is incredibly difficult because they move at ultra-high speeds within the fiery, noisy environment of a jet exhaust, which overwhelms standard sensors. To solve this visual challenge, we developed QuantWear, a quantum-scale optical imaging system that uses quantum sensing to filter out filters out the intense background glare of the engine flames and capture clear 2-D images of the particles by tracking specific atoms attached to their surfaces. We then apply advanced computer vision techniques, using a deep learning framework to accurately detect and count the particles against dynamic, vapour-filled backgrounds. Furthermore, we computationally combine these enhanced 2-D images to reconstruct highly detailed 3-D models of the particles. Our approach empowers computer vision algorithms to achieve exceptional imaging clarity and over 95% accuracy in 3-D shape reconstruction, overcoming the extreme visual interference of jet exhausts. This technology allows engineers to reliably assess jet engine health and detect early signs of damage, which is vital for preventing catastrophic failures and ensuring aircraft safety.