Structural health monitoring feature design by genetic programming

Abstract

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in-situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise.Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for structural health monitoring problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three structural health monitoring applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

“…Feature selection based on a genetic algorithm (GA) is proposed in Ref. [70]; however, the results from a demanding feature selection phase are only comparable to an AR model for damage detection in a simple laboratory structure.…”

Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach

“…The damage localization and quantification steps of SHM have for example been carried out by decomposing the Lamb wave signals using a Proper Generalized Decomposition (PGD) algorithm which is then used to train neural networks in a supervised machine learning framework (Borate et al, 2020). Features automatically extracted by means of genetic algorithms and able to handle Lamb waves for SHM problems have been proposed (Harvey and Todd, 2014). Lamb wave data has also been processed using dynamical wavelet fingerprints and has been demonstrated that features extracted from wavelet analysis of Lamb wave signals can be extremely relevant for SHM purposes (Miller and Hinders, 2014;Hinders and Miller, 2020).…”