Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models
2011
Materials State Awareness (MSA) goes beyond traditional NDE and SHM in its challenge to characterize the current
state of material damage before the onset of macro-damage such as cracks. A highly reliable, minimally invasive system
for MSA of Aerospace Structures, Naval structures as well as next generation space systems is critically needed.
Development of such a system will require a reliable SHM system that can detect the onset of damage well before the
flaw grows to a critical size. Therefore, it is important to develop an integrated SHM system that not only detects macroscale
damages in the structures but also provides an early indication of flaw precursors and microdamages. The early
warning for flaw precursors and their evolution provided by an SHM system can then be used to define remedial
strategies before the structural damage leads to failure, and significantly improve the safety and reliability of the
structures. Thus, in this article a preliminary concept of developing the Hybrid Distributed Sensor Network Integrated
with Self-learning Symbiotic Diagnostic Algorithms and Models to accurately and reliably detect the precursors to
damages that occur to the structure are discussed. Experiments conducted in a laboratory environment shows potential of
the proposed technique.
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