The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering systems is often referred to as structural health monitoring. Vibration-based damage detection is a tool that is receiving considerable attention from the research community for such monitoring. In this paper, the structural health monitoring problem is cast in the context of a statistical pattern recognition paradigm. This pattern recognition process is composed of four portions. (1) operational evaluation, (2) data acquisition & cleansing, (3) feature selection & data compression, and (4) statistical model development. A general discussion of each portion of the process is presented, and the application of this statistical paradigm to two different real world structures, such as a bridge column and a surface-effect fast boat. is studied focusing on the issues of data normalization and feature extraction.
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