An Information-Theoretic Method for Dynamic System Identification With Output-Only Damping Estimation

TL;DR

This paper introduces an information-theoretic approach using entropy and divergence metrics to improve damping estimation accuracy in vibration-based system identification, enhancing real-time monitoring and alert systems.

Contribution

It presents a novel method combining Shannon entropy and Kullback-Leibler divergence for better damping estimates in structural health monitoring.

Findings

Accurately estimates damping in real-time using entropy-based metrics.

Effectively detects damage or anomalies through vibration monitoring.

Validates approach with real-world and benchmark data.

Abstract

The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates due to their empirical nature. This has a significant impact on alert warning systems. This occurs when their duration is misestimated; specifically, when using the vibration amplitudes as an indicator of danger alerts for monitoring systems in damage or anomaly detection scenarios. To this end, approaches based on the Shannon entropy and the Kullback-Leibler divergence concept are proposed. The primary objective is to monitor the vibration levels in near real-time and provide immediate alerts when predefined thresholds are exceeded. In considering the proposed approach, both new real-world data from the multi-axis simulation table at the University of Bath, as well as the benchmark International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring problem are considered. Importantly, the approach is shown to select the optimal model, which accurately captures the correct alert duration, providing a powerful tool for system identification and monitoring.