Damage localization based on symbolic time series analysis

Abstract

Summary The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real-time observation application such as SHM. First, it significantly reduces the dimension of information and provides information-rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2-D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confirmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method needs to be verified by experimental data. Copyright © 2014 John Wiley & Sons, Ltd.