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One of the most critical issues when deploying wireless sensor networks for long-term structural health monitoring (SHM) is the correct and reliable operation of sensors. Sensor faults may reduce the quality of monitoring and, if remaining undetected, might cause significant economic loss due to inaccurate or missing sensor data required for structural assessment and life-cycle management of the monitored structure. This paper presents a fully decentralized approach towards autonomous sensor fault detection and isolation in wireless SHM systems. Instead of physically installing multiple redundant sensors in the monitored structure ("physical redundancy"), which would involve substantial penalties in cost and maintainability, the information inherent in the SHM system is used for fault detection and isolation ("analytical redundancy"). Unlike traditional centralized approaches, the analytical redundancy approach is implemented distributively: Partial models of the wireless SHM system, implemented in terms of artificial neural networks in an object-oriented fashion, are embedded into the wireless sensor nodes deployed for monitoring. In this paper, the design and the prototype implementation of a wireless SHM system capable of autonomously detecting and isolating various types of sensor faults are shown. In laboratory experiments, the prototype SHM system is validated by injecting faults into the wireless sensor nodes while being deployed on a test structure. The paper concludes with a discussion of the results and an outlook on possible future research directions.

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The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This paper presents a life-cycle management (LCM) framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this paper, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internetenabled user interfaces as well as finite element models for system identification and a multiagent system for self-detecting sensor malfunctions. To validate its capabilities and to Smarsly et al. 2 demonstrate its practicability, the framework is deployed for continuous monitoring and lifecycle management of a 500 kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine.

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