Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

An optimized tuned mass damper/harvester device

Much work has been conducted on vibration absorbers, such as tuned mass dampers (TMD), where significant energy is extracted from a structure. Traditionally, this energy is dissipated through the devices as heat. In this paper, the concept of recovering some of this energy electrically and reuse it for structural control or health monitoring is investigated. The energy-dissipating damper of a TMD is replaced with an electromagnetic device in order to transform mechanical vibration into electrical energy. That gives the possibility of controlled damping force whilst generating useful electrical energy. Both analytical and experimental results from an adaptive and a semi-active tuned mass damper/harvester are presented. The obtained results suggest that sufficient energy might be harvested for the device to tune itself to optimise vibration suppression

A wireless ultrasonic NDT sensor system

Ultrasonic condition monitoring technologies have been traditionally utilized in industrial and construction environments where structural integrity is of concern. Such techniques include active systems with either single or multiple transmit-receiver combinations used to obtain defect positioning and magnitude. Active sensors are implemented in two ways; in a thickness operation mode, or as an area-mapping tool operating over longer distances. In addition, passive ultrasonic receivers can be employed to detect and record acoustic emission activity. Existing equipment requires cabling for such systems leading to expensive, complicated installations. This work describes the development and operation of a system that combines these existing ultrasonic technologies with modern wireless techniques within a miniaturized, battery-operated design. A completely wireless sensor has been designed that can independently record and analyze ultrasonic signals. Integrated into the sensor are custom ultrasonic transducers, associated analogue drive and receive electronics, and a Texas Instruments Digital Signal Processor (DSP) used to both control the system and implement the signal processing routines. BlueTooth wireless communication is used for connection to a central observation station, from where network operation can be controlled. Extending battery life is of prime importance and the device employs several strategies to do this. Low voltage transducer excitation suffers from poor signal-to-noise ratios, which can be enhanced by signal processing routines implemented on the DSP. Routines investigated include averaging, digital filtering and pulse compression

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0