Multilayer Cooperative Particle Swarm Optimizer for Feature Selection in Structural Health Monitoring

Abstract

Structural health monitoring (SHM) integrates advanced sensor networks and machine learning (ML) technologies, aiming to automatically extract and identify damage features from sensor data of engineering structures, thus enabling real-time assessment of structural integrity and early diagnosis of potential damage. However, these damage features often include redundant or irrelevant features, which pose challenges for effective feature extraction and damage diagnosis. To solve these problems, a feature selection (FS) algorithm based on multilayer cooperative particle swarm optimizer (MCPSO) is proposed. In MCPSO, the three learning strategies of midpoint sample, random sample, and comprehensive sample are skillfully mixed into the particle swarm optimizer (PSO), and the hierarchical structure is used to update the population. The damage feature subset is optimized by simulating the search process of multilayer particle swarm, and the feature set most sensitive to structural damage is identified to improve the accuracy and reliability of damage detection. Taking the multidamage state monitoring of bolted structure as a verification case, the ultrasonic-guided waves (UGWs) signals of bolted structure in different states are collected by lead zirconate titanate sensors. The experimental results show that compared with the ML algorithm, MCPSO can select a stable and effective feature subset from the noise data and realize the identification and quantification of various damage states, such as health, crack, loosening, and loosening-crack composite damage, which provides a universal method for the technical development and engineering practice in the field of SHM.