Structural Damage Detection using Independent Component Analysis

Abstract

This paper presents a novel approach to detect structural damage based on combining independent component analysis (ICA) extraction of time domain data and artificial neural networks (ANN). The advantage of using time history measurements is that the original vibration information is used directly. However, the volume of data, measurement noise and the lack of reliable feature extraction tools are the major obstacles. To circumvent them, the independent component analysis technique is applied to represent the measured data with a linear combination of dominant statistical independent components and the mixing matrix [ A]. Such a representation captures the essential structure of the measured vibration data. The vibration features represented by the mixing matrix provide the relationship between the measured vibration response and the independent components and are then employed to build the simplified neural network model for damage detection. Two examples are included to demonstrate the effectiveness of the method. First, a truss structure with simulated displacement data was used, and the results show that healthy and damage states located in the nine elements may be classified. Second, a bookshelf structure together with measured time history data from 24 piezoelectric single axis accelerometers was used to demonstrate the approach on a physical structure. The results show the successful detection of the undamaged and damaged states with very good accuracy and repeatability.

Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis

“…Structural parameters such as measured flexibility and/or stiffness matrix constructed from identified modal parameters may also be used for damage detection and localisation [2,3]. There is also a large amount of research using other damage-sensitive features without the need to identify the modal parameters, such as novelty analysis with auto-regressive models [4,5], principal component analysis [6,7], w 2 -test based on realisation analysis [8], independent component analysis [9], and more recently a novelty analysis based on a Kalman model [10], etc.…”

Output-only modal analysis using blind source separation techniques

“…They also enjoyed various applications in structural dynamics, including damage detection [5], condition monitoring [6,7] and discrimination between pure tones and sharp-pointed resonances [8]. A special issue dealing with BSS was published in Mechanical Systems and Signal Processing in 2005 [9].…”

Structural damage localization by combining flexibility and stiffness methods

“…Damage may also be detected on the basis of other damage-sensitive effects without the need of identifying modal parameters (see, for example, Refs. [1][2][3][4][5][6]). Damage causes changes in structural parameters, namely the matrices of mass, damping, stiffness and flexibility of structures.…”