Stuart G. Taylor

A mobile-agent-based wireless sensing network for structural monitoring applications

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.

Diagnostics for piezoelectric transducers under cyclic loads deployed for structural health monitoring applications

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. (Some figures may appear in colour only in the online journal)

Full-scale fatigue tests of CX-100 wind turbine blades. Part I: testing

This paper overviews the test setup and experimental methods for structural health monitoring (SHM) of two 9-meter CX-100 wind turbine blades that underwent fatigue loading at the National Renewable Energy Laboratorys (NREL) National Wind Technology Center (NWTC). The first blade was a pristine blade, which was manufactured to standard specifications for the CX-100 design. The second blade was manufactured for the University of Massachusetts, Lowell with intentional simulated defects within the fabric layup. Each blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges. The blades underwent harmonic excitation at their first natural frequency using the Universal Resonant Excitation (UREX) system at NREL. Blades were initially excited at 25% of their design load, and then with steadily increasing loads until each blade reached failure. Data from the sensors were collected between and during fatigue loading sessions. The data were measured over multi-scale frequency ranges using a variety of acquisition equipment, including off-the-shelf systems and specially designed hardware developed at Los Alamos National Laboratory (LANL). The hardware systems were evaluated for their aptness in data collection for effective application of SHM methods to the blades. The results of this assessment will inform the selection of acquisition hardware and sensor types to be deployed on a CX-100 flight test to be conducted in collaboration with Sandia National Laboratory at the U.S. Department of Agricultures (USDA) Conservation and Production Research Laboratory (CPRL) in Bushland, Texas.

Fatigue crack detection performance comparison in a composite wind turbine rotor blade

This article presents the detection performance results for multiple detectors or test statistics, using different active-sensing hardware systems in identifying the presence and location of a through-thickness fatigue crack in a 9-m composite wind turbine rotor blade. The rotor blade underwent ~8.5 million cycles of fatigue loading until failure, when a 30-cm-long crack surfaced on the leading edge portion of the blade’s transitional root area. The rotor blade was cantilevered on a 7-ton test stand and excited using a hydraulically actuated resonant excitation system, which drove the rotor blade at its first natural frequency. Through the course of the test, data were collected using two distinct types of acquisition hardware: one designed for ultrasonic-guided wave interrogation and the other for diffuse wave field interrogation. This article presents the fatigue crack detection performance results for several hardware and test statistic combinations.

Incipient crack detection in a composite wind turbine rotor blade

This article presents a performance optimization approach to incipient crack detection in a wind turbine rotor blade that underwent fatigue loading to failure. The objective of this article is to determine an optimal demarcation date, which is required to properly normalize active-sensing data collected and processed using disparate methods for the purpose of damage detection performance comparison. We propose that maximizing average damage detection performance with respect to a demarcation date would provide both an estimate of the true incipient damage onset date and the proper normalization enabling comparison of detection performance among the otherwise disparate data sets. This work focuses on the use of ultrasonic guided waves to detect incipient damage prior to the surfacing of a visible, catastrophic crack. The blade was instrumented with piezoelectric transducers, which were used in a pitch-catch mode over a range of excitation frequencies. With respect to specific excitation frequencies and transmission paths, higher excitation frequencies provided consistent detection results for paths along the rotor blade’s carbon fiber spar cap, but performance fell off with increasing excitation frequency for paths not along the spar cap. Lower excitation frequencies provided consistent detection performance among all sensor paths.

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex bottleneck structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a grand challenge due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.

Wireless impedance device for electromechanical impedance sensing and low-frequency vibration data acquisition

This paper presents recent developments in an extremely compact, wireless impedance sensor node for combined use with both impedance method and low-frequency vibrational data acquisition. The sensor node, referred to as the WID3 (Wireless Impedance Device) integrates several components, including an impedance chip, a microcontroller for local computing, telemetry for wireless data transmission, multiplexers for managing up to seven piezoelectric transducers per node, energy storage mediums, and several triggering options into one package to truly realize a self-contained wireless active-sensor node for SHM applications. Furthermore, we recently extended the capability of this device by implementing low-frequency A/D and D/A converters so that the same device can measure low-frequency vibration data. The WID3 requires less than 60 mW of power to operate and is designed for the mobile-agent based wireless sensing network. The performance of this miniaturized device is compared to our previous results and its capabilities are demonstrated.

Damage Identification of Wind Turbine Blades Using Piezoelectric Transducers

This paper presents the experimental results of active-sensing structural health monitoring (SHM) techniques, which utilize piezoelectric transducers as sensors and actuators, for determining the structural integrity of wind turbine blades. Specifically, Lamb wave propagations and frequency response functions at high frequency ranges are used to estimate the condition of wind turbine blades. For experiments, a 1 m section of a CX-100 blade is used. The goal of this study is to assess and compare the performance of each method in identifying incipient damage with a consideration given to field deployability. Overall, these methods yielded a sufficient damage detection capability to warrant further investigation. This paper also summarizes the SHM results of a full-scale fatigue test of a 9 m CX-100 blade using piezoelectric active sensors. This paper outlines considerations needed to design such SHM systems, experimental procedures and results, and additional issues that can be used as guidelines for future investigations.

Structural health monitoring of wind turbine blades under fatigue loads

This paper presents the results of dynamic characterization and preparation of a full-scale fatigue test of a 9 m CX-100 blade. Sensors and actuators utilized include accelerometers and piezoelectric sensors. To dynamically characterize a 9 m CX-100 blade, full scale modal analyses were completed with varying boundary conditions and blade orientations. Also, multi-scale sensing damage detection techniques were explored; high frequency active-sensing was used in identifying fatigue damage initiation, while low frequency passive-sensing was used in assessing damage progression. Ultimately, high and low frequency response functions, wave propagations, and sensor diagnostic methods were utilized to monitor and analyze the condition of the wind turbine blade under fatigue loading.

Application of a Wireless Sensor Node to Health Monitoring of Operational Wind Turbine Blades

Structural health monitoring (SHM) is a developing field of research with a variety of applications including civil structures, industrial equipment, and energy infrastructure. An SHM system requires an integrated process of sensing, data interrogation and statistical assessment. The first and most important stage of any SHM system is the sensing system, which is traditionally composed of transducers and data acquisition hardware. However, such hardware is often heavy, bulky, and difficult to install in situ. Furthermore, physical access to the structure being monitored may be limited or restricted, as is the case for rotating wind turbine blades or unmanned aerial vehicles, requiring wireless transmission of sensor readings. This study applies a previously developed compact wireless sensor node to structural health monitoring of rotating small-scale wind turbine blades. The compact sensor node collects low-frequency structural vibration measurements to estimate natural frequencies and operational deflection shapes. The sensor node also has the capability to perform high-frequency impedance measurements to detect changes in local material properties or other physical characteristics. Operational measurements were collected using the wireless sensing system for both healthy and damaged blade conditions. Damage sensitive features were extracted from the collected data, and those features were used to classify the structural condition as healthy or damaged.