INSTITUTE OF PHYSICS PUBLISHING SMART MATERIALS AND STRUCTURES Smart Mater. Struct. 10 (2001) 446–451 www.iop.org/Journals/sm PII: S0964-1726(01)22811-0 Damage diagnosis using time series analysis of vibration signals Hoon Sohn 1 and Charles R Farrar 2 Engineering Analysis Group (ESA-EA), M/S P946 Los Alamos National Laboratory, Los Alamos, NM 87545, USA E-mail: sohn@lanl.gov and farrar@lanl.gov Received 15 September 2000 Abstract A novel time series analysis is presented to locate damage sources in a mechanical system, which is running in various operational environments. The source of damage is located by solely analyzing the acceleration time histories recorded from a structure of interest. First, a data normalization procedure is proposed. This procedure selects a reference signal that is ‘closest’ to a newly obtained signal from an ensemble of signals recorded when the structure is undamaged. Second, a two-stage prediction model (combining auto-regressive (AR) and auto-regressive with exogenous inputs (ARX) techniques) is constructed from the selected reference signal. Then, the residual error, which is the difference between the actual acceleration measurement for the new signal and the prediction obtained from the AR–ARX model developed from the reference signal, is defined as the damage-sensitive feature. This approach is based on the premise that if there were damage in the structure, the prediction model previously identified using the undamaged time history would not be able to reproduce the newly obtained time series measured from the damaged structure. Furthermore, the increase in residual errors would be maximized at the sensors instrumented near the actual damage locations. The applicability of this approach is demonstrated using acceleration time histories obtained from an eight degrees-of-freedom mass–spring system. (Some figures in this article are in colour only in the electronic version; see www.iop.org) 1. Introduction The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system’s performance. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. For long-term SHM, the output of this process is periodically updated information regarding the ability of the structure to perform its intended function in light of the inevitable aging and degradation resulting from operational environments. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in near real time, reliable information regarding the integrity of the structure. A recent collapse of a pedestrian walkway bridge in North Carolina, USA (http:// www.cnn.com/2000/US/05/21/racetrack.collapse/index.html) has received a tremendous amount of media attention, emphasizing the importance of health and condition monitoring for such structures. Furthermore, major advances in sensor technology and wireless data transmission are making the development of such a monitoring system economically feasible. Based on the work of Rytter (1993), the authors categorize the structural health monitoring process into five stages: (1) identification of damage presence in a structure, (2) localization of damage, (3) identification of the damage type, (4) quantification of damage severity, and (5) prediction of the remaining service life of the structure. Doebling et al 0964-1726/01/030446+06$30.00 © 2001 IOP Publishing Ltd Printed in the UK 446