In this paper, we will summarize our efforts on exploring guided acoustic waves generated by MEMS ultrasonic
transducers enabling a non-destructive, ultra-low powered, wireless SHM system. State-of-the-art SHM systems employ
bulk piezoelectric transducers. However, they are not environmentally benign (contain lead), not cost feasible for
monitoring every bridge in the U.S., require significant power for operation, lack integration capability for wireless
interrogation, need precise matching layers, and have only 25-50 percent fractional bandwidth, limiting the detection
resolution. To alleviate most of these shortcomings, a low impedance MEMS transducer, called a capacitive
micromachined ultrasonic transducer (CMUT), is explored.
KEYWORDS: Autoregressive models, Data modeling, Mahalanobis distance, Structural health monitoring, Pattern recognition, Detection and tracking algorithms, Statistical analysis, Time series analysis, Data analysis, Aerospace engineering
Identification of damage in a structure, or structural change in general, has been a challenging problem for the
researchers in Structural Health Monitoring (SHM) area. Over the last a few decades, a number of experimental and
analytical techniques have been developed and used to solve such problem. It has been has been recently accepted in the
literature that the process of damage identification problem is one where statistical pattern recognition techniques can be
of use because of the inherent uncertainties of the problem. Time series analysis is one of the methods, which is
implemented in statistical pattern recognition applications to SHM. In previous studies, Auto-Regressive (AR) models
are highly utilized for this purpose. In this study, AR model coefficients are used with different outlier detection and
clustering algorithms to detect the change in the boundary conditions of a steel beam. A number of different boundary
conditions are realized by using different types and amounts of elastomeric pads. The advantages and the shortcomings
of the methodology are discussed in detail based on the experimental results in terms of the ability of it to detect the
structural changes and localize them.
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