Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

Rongshuai Li,Akira Mita,Jin Zhou

doi:10.4236/jilsa.2013.51006

Abstract

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

Highlights

Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades
The training data sets that were to generate detectors were acceleration time histories from the top story of the healthy shear structure under ground motion following a pattern of randomly generated Gaussian white noise
The test data sets were obtained from the top story of the healthy and damaged shear structure under ground motion following another pattern of randomly generated Gaussian white noise

Summary

Introduction

Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades. The focus of SHM research is the detection, localization, and quantification of damage in a variety of structures. SHM techniques for detecting, localizing, and quantifying damage rely on measuring the structural response to ambient vibrations or forced excitations. SHM techniques infer the existence, location and severity of damage by detecting differences in local or global structural responses before and after the damage occurs. An improved clonal selection algorithm (CSA), called adaptive immune CSA (AICSA), has been used for structural damage localization and quantification [4,5]. A novel pattern identification technique, called symbolic time series analysis (STSA), was developed. Several case studies [7,8,9] have shown that STSA is more effective at anomaly detection than pattern recognition techniques such as principal component analysis and neural networks. STSA has been used for fault detection in electromechanical systems, e.g., three-phase induction motors [10]

Methods

Results

Conclusion