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The process of implementing a damage identification 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. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

In this paper we summarize the hardware and software issues of impedance-based structural health monitoring based on piezoelectric materials. The basic concept of the method is to use high-frequency structural excitations to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. A brief overview of research work on experimental and theoretical studies on various structures is considered and several research papers on these topics are cited. This paper concludes with a discussion of future research areas and path forward.

Many aerospace, civil and mechanical systems continue to be used despite ageing and the associated potential for damage accumulation. Therefore, the ability to monitor the structural health of these systems is becoming increasingly important. A wide variety of highly effective local non-destructive evaluation tools is available. However, damage identification based upon changes in vibration characteristics is one of the few methods that monitor changes in the structure on a global basis. A summary of developments in the field of global structural health monitoring that have taken place over the last thirty years is first presented. Vibration-based damage detection is a primary tool that is employed for this monitoring. Next, the process of vibrationbased damage detection will be described as a problem in statistical pattern recognition. This process is composed of three portions: (i) data acquisition and cleansing; (ii) feature selection and data compression; and (iii) statistical model development. Current research regarding feature selection and statistical model development will be emphasized with the application of this technology to a large-scale laboratory structure.

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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.

Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.