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Proceedings Article

Structural damage detection with insufficient data using transfer learning techniques

[+] Author Affiliations
Debejyo Chakraborty

General Motors Corp. (USA)

Narayan Kovvali, Bhavana Chakraborty, Antonia Papandreou-Suppappola, Aditi Chattopadhyay

Arizona State Univ. (USA)

Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798147 (April 18, 2011); doi:10.1117/12.882025
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From Conference Volume 7981

  • Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
  • Masayoshi Tomizuka
  • San Diego, California, USA | March 06, 2011

abstract

The effective detection and classification of damage in complex structures is an important task in the realization of structural health monitoring (SHM) systems. Conventional information processing techniques utilize statistical modeling machinery that requires large amounts of 'training' data which is usually difficult to obtain, leading to compromised system performance under these data-scarce conditions. However, in many SHM scenarios a modest amount of data may be available from a few different but related experiments. In this paper, a new structural damage classification method is proposed that makes use of statistics from related task(s) to improve the classification performance on a data set with limited training examples. The approach is based on the framework of transfer learning (TL) which provides a mechanism for information transfer between related learning tasks. The utility of the proposed method is demonstrated for the classification of fatigue damage in an aluminum lug joint.

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

Debejyo Chakraborty ; Narayan Kovvali ; Bhavana Chakraborty ; Antonia Papandreou-Suppappola and Aditi Chattopadhyay
"Structural damage detection with insufficient data using transfer learning techniques", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798147 (April 18, 2011); doi:10.1117/12.882025; http://dx.doi.org/10.1117/12.882025


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