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A new wireless sensing network paradigm is presented for structural monitoring applications. In this approach, both power and data interrogation commands are conveyed via a mobile agent that is sent to sensor nodes to perform intended interrogations, which can alleviate several limitations of the traditional sensing networks. Furthermore, the mobile agent provides computational power to make near real-time assessments on the structural conditions. This paper will discuss such prototype systems, which are used to interrogate impedance-based sensors for structural health monitoring applications. Our wireless sensor node is specifically designed to accept various energy sources, including wireless energy transmission, and to be wirelessly triggered on an as-needed basis by the mobile agent or other sensor nodes. The capabilities of this proposed sensing network paradigm are demonstrated in the laboratory and the field.

Accurate sensor self-diagnostics are a key component of successful structural health monitoring (SHM) systems. Transducer failure can be a significant source of failure in SHM systems, and neglecting to incorporate an adequate sensor diagnostics capability can lead to false positives in damage detection. Any permanently installed SHM system will thus require the ability to accurately monitor the health of the sensors themselves, so that when deviations in baseline measurements are observed, one can clearly distinguish between structural changes and sensor malfunction. This paper presents an overview of sensor diagnostics for active-sensing SHM systems employing piezoelectric transducers, and it reviews the sensor diagnostics results from an experimental case study in which a 9 m wind turbine rotor blade was dynamically loaded in a fatigue test until reaching catastrophic failure. The fatigue test for this rotor blade was unexpectedly long, requiring more than 8 million fatigue cycles before failure. Based on previous experiments, it was expected that the rotor blade would reach failure near 2 million fatigue cycles. Several sensors failed in the course of this much longer than expected test, although 48 out of 49 installed piezoelectric transducers survived beyond the anticipated 2 million fatigue cycles. Of the transducers that did fail in the course of the test, the sensor diagnostics methods presented here were effective in identifying them for replacement and/or data cleansing. Finally, while most sensor diagnostics studies have been performed in a controlled, static environment, some data in this study were collected as the rotor blade underwent cyclic loading, resulting in nonstationary structural impedance. This loading condition motivated the implementation of a new, additional data normalization step for sensor diagnostics with piezoelectric transducers in operational environments.

In this paper, we combine recent developments in modeling of fatigue-damage, isogeometric analysis (IGA) of thin-shell structures, and structural health monitoring (SHM) to develop a computational steering framework for fatigue-damage prediction in full-scale laminated composite structures. The main constituents of the proposed framework are described in detail, and the framework is deployed in the context of an actual fatigue test of a full-scale wind-turbine blade structure. The results indicate that using an advanced computational model informed by in situ SHM data leads to accurate prediction of the damage zone formation, damage progression, and eventual failure of the structure. Although the blade fatigue simulation was driven by test data obtained prior to the computation, the proposed computational steering framework may be deployed concurrently with structures undergoing fatigue loading.