A statistical pattern recognition paradigm for structural health monitoring
- Charles R.
- Hoon
- Gyu Hae
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 current or future performance. Our approach is to address the SHM problem in the context of a statistical pattern recognition paradigm (Farrar, Nix and Doebling, 2001). In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition, (3) Feature Extraction, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from 'real-world' structures, it quickly becomes apparent that data cleansing, normalization, fusion and compression, which can be implemented with either hardware or software, are inherent in Parts 2-4 of this paradigm. The authors believe that all approaches to SHM, as well as all traditional non-destructive evaluation procedures (e.g. ultrasonic inspection, acoustic emissions, active thermography) can be cast in the context of this statistical pattern recognition paradigm. It should be noted that the statistical modeling portion of the structural health monitoring process has received the least attention in the technical literature. The algorithms used in statistical model development usually fall into the three categories of group classification, regression analysis or outlier detection. The ability to use a particular statistical procedure from one of these categories will depend on the availability of data from both an undamaged and damaged structure. This paper will discuss each portion of the SHM statistical pattern recognition paradigm.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 977741
- Report Number(s):
- LA-UR-04-4465; TRN: US201012%%524
- Resource Relation:
- Conference: Submitted to: 9th ACSE Specialty Conference on Probabilistic Mechanics and Structural Reliability, Albuquerque, NM, July 26-28, 2004.
- Country of Publication:
- United States
- Language:
- English
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