Spectral Jump Anomaly Detection: Temperature-compensated algorithm for structural damage detection using vibration data

Abstract

Assessing the integrity of structural systems throughout their aging process has capital importance in infrastructure management. Monitoring these infrastructures presents challenges in distinguishing early damage from slight variations in the structural behavior caused by environmental or operational variability.

This paper introduces the Spectral Jump Anomaly Detection (SJ-AD) algorithm, a data-driven method designed to identify minor structural damage using acceleration collected under considerable environmental variability. SJ-AD focuses on anomalies in the distribution of a distance measure, the minimum jump cost, calculated between power spectra. The method effectively identifies issues in the KW-51 bridge, even with minimal structural defects and varying temperatures. Additionally, numerical experiments show that SJ-AD can detect low damping variations in noisy conditions, demonstrating robustness against minor frequency changes. Its flexible approach and sensitivity to small damages make SJ-AD a promising solution for proactive maintenance and risk management in various structural systems.