Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models

[+] Author Affiliations
Sourav Banerjee, Shawn Beard

Acellent Technologies, Inc. (USA)

Lie Liu, S. T. Liu, Fuh-Gwo Yuan

North Carolina State Univ. (USA)

Proc. SPIE 7984, Health Monitoring of Structural and Biological Systems 2011, 79840M (March 31, 2011); doi:10.1117/12.880511
Text Size: A A A
From Conference Volume 7984

  • Health Monitoring of Structural and Biological Systems 2011
  • Tribikram Kundu
  • San Diego, California, USA | March 06, 2011

abstract

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.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Sourav Banerjee ; Lie Liu ; S. T. Liu ; Fuh-Gwo Yuan and Shawn Beard
"Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models", Proc. SPIE 7984, Health Monitoring of Structural and Biological Systems 2011, 79840M (March 31, 2011); doi:10.1117/12.880511; http://dx.doi.org/10.1117/12.880511


Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).

Figures

Tables

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.