Leveraging Gaussian Process and Voting-Empowered Many-Objective Evaluation for Fault Identification

TL;DR

This paper introduces a novel data-assisted optimization method using Gaussian processes and a voting mechanism for efficient fault identification in structures via impedance measurements, avoiding computationally intensive finite element analysis.

Contribution

It develops a many-objective optimization framework with Gaussian process response surfaces and a voting scheme to improve fault detection accuracy and efficiency.

Findings

Effective fault identification demonstrated through numerical case studies.

The approach reduces computational costs compared to traditional methods.

Voting scores improve solution reliability and interpretability.

Abstract

Using piezoelectric impedance/admittance sensing for structural health monitoring is promising, owing to the simplicity in circuitry design as well as the high-frequency interrogation capability. The actual identification of fault location and severity using impedance/admittance measurements, nevertheless, remains to be an extremely challenging task. A first-principle based structural model using finite element discretization requires high dimensionality to characterize the high-frequency response. As such, direct inversion using the sensitivity matrix usually yields an under-determined problem. Alternatively, the identification problem may be cast into an optimization framework in which fault parameters are identified through repeated forward finite element analysis which however is oftentimes computationally prohibitive. This paper presents an efficient data-assisted optimization approach for fault identification without using finite element model iteratively. We formulate a many-objective optimization problem to identify fault parameters, where response surfaces of impedance measurements are constructed through Gaussian process-based calibration. To balance between solution diversity and convergence, an -dominance enabled many-objective simulated annealing algorithm is established. As multiple solutions are expected, a voting score calculation procedure is developed to further identify those solutions that yield better implications regarding structural health condition. The effectiveness of the proposed approach is demonstrated by systematic numerical and experimental case studies.