A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 10168, Issue , 2017, Pages

  Salehi, Hadi ^a   Das, Saptarshi ^a   Chakrabartty, Shantanu ^b   Biswas, Subir ^a   Burgueño, Rigoberto ^a  

^a Michigan State University   (United States)

^b WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

Author keywords

aerospace structures; Damage identification; machine learning; pattern recognition; self powered sensors

Indexed keywords

AIRCRAFT DETECTION; AIRCRAFT MANUFACTURE; AIRFRAMES; ARTIFICIAL INTELLIGENCE; BINS; BUDGET CONTROL; DATA FUSION; ENERGY EFFICIENCY; FINITE ELEMENT METHOD; HORIZONTAL STABILIZERS; INFORMATION MANAGEMENT; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION; POWER MANAGEMENT; POWER MANAGEMENT (TELECOMMUNICATION); TIME DELAY;

AEROSPACE STRUCTURE; DAMAGE IDENTIFICATION; DAMAGE-DETECTION STRATEGY; ENERGY EFFICIENT TECHNOLOGY; INFORMATION DELIVERY; MACHINE LEARNING APPROACHES; NOISY AND INCOMPLETE DATUM; SELF-POWERED;

DAMAGE DETECTION;

EID: 85029902191     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2260118     Document Type: Conference Paper

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