Monitoring Electrical Transformer Mechanics via Crowdsourced Imaging

Abstract

Electrical transformers are critical to power grids, and their mechanical integrity directly impacts system stability. Existing monitoring approaches, such as accelerometer, acoustic, and piezoelectric sensing, require intrusive installation and are often costly or impractical for real-time assessment. To overcome these limitations, we propose Transcope, a computational imaging framework that remotely infers transformer mechanical states by analyzing the illumination variations of a transformer-connected bulb. The key principle is vibration-to-flicker coupling, where abnormal mechanical states induce voltage ripples that modulate bulb brightness and create a visual proxy for internal vibrations. To this end, we develop a flicker-aware physical model to characterize the mapping from bulb images to flicker signals, and further design a diffusion-based framework to reconstruct vibration signals from weak visual observations. Extensive experiments on a real-world testbed with more than 100k samples show that Transcope achieves 0.070 MAE and 19.08 dB SNR in vibration reconstruction, and reaches 98.79% accuracy and 96.19% macro F1-score in anomaly diagnosis. These results demonstrate the promise of reliable, non-contact transformer monitoring using ordinary cameras.