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This paper discusses the development status of a self-powered wireless sensor node for steel and concrete bridges monitoring and prognosis. By the end of the third year in this four-year cross-disciplinary project, the 4-channel acoustic emission wireless node, developed by Mistras Group Inc, has already been deployed in concrete structures by the University of Miami. Also, extensive testing is underway with the node powered by structural vibration and wind energy harvesting modules developed by Virginia Tech. The development of diagnosis tools and models for bridge prognosis, which will be discussed in the paper, continues and the diagnosis tools are expected to be programmed in the node's AVR during the 4th year of the project. The impact of this development extends beyond the area of bridge health monitoring into several fields, such as offshore oil platforms, composite components on military ships and race boats, combat deployable bridges and wind turbine blades. Some of these applications will also be discussed. This project was awarded to a joint venture formed by Mistras Group Inc, Virginia Tech, University of South Carolina and University of Miami by the National Institute of Standards and Technology through its Technology Innovation Program Grant #70NANB9H007

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Diaphragm-type couplings are high misalignment torque and speed transfer components used in aircrafts. Crack development in such couplings, or in the drive train in general, can lead to component failure that can bring down an aircraft. Real time detection of crack formation and growth is important to prevent such catastrophic failures. However, there is no single Nondestructive Monitoring method available that is capable of assessing the early stages of crack growth in such components. While vibration based damage identification techniques are used, they cannot detect cracks until they reach a considerable size, which makes detection of the onset of cracking extremely difficult. Acoustic Emission (AE) can detect and monitor early stage crack growth, however excessive background noise can mask acoustic emissions produced by crack initiation. Fusion of the two mentioned techniques can increase the accuracy of measurement and minimize false alarms. However, a monitoring system combining both techniques could prove too large and heavy for the already restricted space available in aircrafts.In the present work, we will present a newly developed integrated Acoustic Emission/Vibration (AE/VIB) combined sensor which can operate in the temperature range of -55°F to 257°F and in high EMI environment. This robust AE/VIB sensor has a frequency range of 5 Hz-2 kHz for the vibration component and a range of 200-400 kHz for the acoustic emission component. The sensor weight is comparable to accelerometers currently used in flying aircraft. Traditional signal processing approaches are not effective due to high signal attenuation and strong background noise conditions, commonly found in aircraft drive train systems. As an alternative, we will introduce a new Supervised Pattern Recognition (SPR) methodology that allows for simultaneous processing of the signals detected by the AE/VIB sensor and their classification in near-real time, even in these adverse conditions. Finally, we will discuss the architecture developed to produce a fully autonomous monitoring tool based on the fusion of the AE and Vibration techniques.

This paper presents the results obtained during the development of a semi-real-time monitoring methodology based on Neural Network Pattern Recognition of Acoustic Emission (AE) signals for early detection of cracks in couplings used in aircraft and engine drive systems. AE signals were collected in order to establish a baseline of a gear-testing fixture background noise and its variations due to rotational speed and torque. Also, simulated cracking signals immersed in background noise were collected. EDM notches were machined in the driving gear and the load on the gearbox was increased until damaged was induced. Using these data, a Neural Network Signal Classifier (NNSC) was implemented and tested. The testing showed that the NNSC was capable of correctly identifying six different classes of AE signals corresponding to different gearbox operation conditions. Also, a semi-real-time classification software was implemented. This software includes functions that allow the user to view and classify AE data from a dynamic process as they are recorded at programmable time intervals. The software is capable of monitoring periodic statistics of AE data, which can be used as an indicator of damage presence and severity in a dynamic system. The semi-real-time classification software was successfully tested in situations where a delay of 10 seconds between data acquisition and classification was achieved with a hit rate of 50 hits/second per channel on eight active AE channels.