Title :
Enhancing vibration analysis by embedded sensor data validation technologies
Author :
Maldonado, Francisco J. ; Oonk, Stephen ; Politopoulos, Tasso
Author_Institution :
American GNC Corp., Simi Valley, CA, USA
Abstract :
This paper discusses a Structural Health Monitoring framework developed for aircraft airframes, where the objective is high performance vibration-based diagnostics using validated data from low power and miniaturized smart sensors. Although considerable research has been devoted to the structural health monitoring discipline, successful field implementations have not been widely achieved. This research presents a new embedded solution by integrating several state-of-the-art technologies. The system architecture is divided into two levels, with the low level built on embedded smart sensors capable of: self-diagnostics; high performance data acquisition; advanced vibration analysis; embedded admittance measurements; elastic wave generation; and wireless communications. A key capability is sensor data validation using an electromechanical impedance method, where failures in piezoelectric transducer elements as well as damage to the host structure are detected. Then, at the next level is a computation system hosting a graphical user interface with visualization methods, a feature extraction toolset, and advanced artificial neural network diagnostics. The overall goal of this research effort was to develop a system architecture with smart sensors and intelligent processing to be deployed in aircraft for the detection and isolation of global and incipient failures.
Keywords :
aerospace components; computerised monitoring; condition monitoring; data acquisition; data visualisation; elastic waves; electric admittance measurement; failure analysis; fault diagnosis; feature extraction; graphical user interfaces; intelligent sensors; neural nets; piezoelectric transducers; radiation effects; structural engineering; vibrations; advanced artificial neural network diagnostic; aircraft airframe; damage detection; data acquisition; data validation technology; elastic wave generation; electromechanical impedance method; embedded admittance measurement; embedded smart sensor; failure detection; failure isolation; feature extraction toolset; graphical user interface; intelligent processing; piezoelectric transducer; self-diagnostics; structural health monitoring; vibration analysis; vibration-based diagnostic; visualization method; wireless communication; Admittance; Artificial neural networks; Feature extraction; Monitoring; Multiresolution analysis; Time frequency analysis; Vibrations; Data Validation; Embedded Techniques; Smart Sensors; Structural Health Monitoring; Vibration Analysis;
Conference_Titel :
AUTOTESTCON, 2012 IEEE
Conference_Location :
Anaheim, CA
Print_ISBN :
978-1-4673-0698-0
DOI :
10.1109/AUTEST.2012.6334522