Mannur J. Sundaresan

Structural health monitoring techniques for wind turbine blades

Abstract Wind turbine blades are made of fiberglass material to be cost effective, but they can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. It is important to detect the damage before the blade fails catastrophically which could destroy the entire wind turbine. In this paper, four different algorithms are tested for detecting damage on wind turbine blades. These are the transmittance function, resonant comparison, operational deflection shape, and wave propagation methods. The methods are all based on measuring the vibration response of the blade when it is excited using piezoceramic actuator patches bonded to the blade. The vibration response of the blade is measured using either piezoceramic sensor patches bonded to the blade, or a scanning laser doppler vibrometer. The sensitivity of the techniques to detect a reversible damage simulated by a steel plate clamped to a section of a wind turbine blade is compared in this paper.

Piezoelectric materials at elevated temperature

Piezoelectric ceramic patches are the mainstay for actuating and sensing in smart structures, but these patches are limited in the temperature range in which they can operate. Operation at temperatures above ambient is desired for new applications of smart structures, including in aircraft, turbine engine components, space vehicles, and others. The decrease in the actuation and sensing capability with increasing temperature is mainly due to the loss of the piezoelectric properties through depoling. The decrease in performance is also due to the compliance of the adhesive used to bond the patch to the structure, the insulating film covering the patch, and the inside adhesive bonding the film to the wafer. This paper discusses the general properties and modeling of piezoelectric materials, and then experimentally characterizes the performance of piezoelectric ceramic patches used as sensors bonded onto an aluminum beam operating at moderately elevated temperatures. It is shown that the piezoelectric property of the sensor decreases with increasing temperature until the properties are almost completely lost, and also the piezoelectric properties return each time when the sensor is cooled. Finally, high temperature nanotechnology is investigated as an approach that might replace piezoelectric ceramics for sensing and actuating of smart structures at high temperatures.

Active fiber composites for structural health monitoring

An embedded active fiber composite tape was investigated for use as a sensor for structural health monitoring. The material has unidirectional piezoceramic fibers with interdigital electrodes on the top and bottom surfaces and is poled in the fiber direction. A long active fiber composite tape with segmented electrodes was modeled as a sensor to measure longitudinal stress waves in a uniform cantilever bar. Only plane longitudinal standing and traveling waves in the bar are modeled. The sensor was connected to an electrical tuning circuit to filter out undesirable noise due to ambient vibrations. The elastic response of the bar was compute in closed form at small time steps, and the coupled piezoceramic constitutive equations and the electrical circuit equations were solved by numerical integration using the Newmark-Beta method. Strain vibration and wave propagation responses were computed in the simulations. The simulations indicate that such a sensor will be capable of detecting damage to the bar from the change sin the wave propagation responses. Further, the sensor has adequate sensitivity to detect fiber breaks in the composite bar. An active fiber composite patch was also tested to measure vibration and simulated acoustic emissions.

A Structural Neural System for Real-time Health Monitoring of Composite Materials

A prototype structural neural system (SNS) is tested for the first time and damage detection results are presented in this study. The SNS is a passive online structural health monitoring (SHM) system that mimics the synaptic parallel computation networks present in the human biological neural system. Piezoelectric ceramic sensors and analog electronics are used to form neurons that measure strain waves generated by damage. The sensing of strain waves is similar to the proven nondestructive evaluation (NDE) technique of acoustic emission (AE) monitoring. Fatigue testing of a composite specimen on a four-point bending fiXture is performed, and the SNS is used to monitor the specimen for damage in real time. The prototype SNS used four sensors as inputs, but the number of inputs can be in the tens or hundreds depending on the type of SNS processor used. This is an area of continuing development. The SNS has two channels of signal output that are digitized and processed in a computer. The first output channel tracks the propagation of waves due to damage, and the second output channel provides the combined AE responses of the sensors. The data from these two channels are used to predict the location of damage and to qualitatively indicate the severity of the damage. Overall, this study shows that the SNS can detect damage growth in composites during operation of the structure, and the SNS architecture has the potential to tremendously simplify the AE technique for use in on-board SHM. Ten or more input neurons can be used, and still only two output channels are needed. Two levels of monitoring are possible using the SNS; a coarser SHM approach, or an on-board NDE approach. The SHM approach uses the SNS with a coarse grid of neurons to monitor and detect damage occurring in a general area during operation of the structure. The SNS will indicate where and when a more sensitive inspection is needed which can be done using ground-based NDE techniques. The on-board NDE approach uses the SNS with a fine coverage of neurons for highly sensitive NDE which continuously listens for damage and provides real-time processing and information about any damage in the structure and the performance limits and safety of the vehicle.

Neural system for structural health monitoring

This is an overview paper that discusses the concept of an embeddable structural health monitoring system for use in composite and heterogeneous material systems. The sensor system is formed by integrating groups of autonomous unit cells into a structure, much like neurons in biological systems. Each unit cell consists of an embedded processor and a group of distributed sensors that gives the structure the ability to sense damage. In addition, each unit cell periodically updates a central processor on the status of health in its neighborhood. This micro-architectured synthetic nervous system has an advanced sensing capability based on new continuous sensor technology. This technology uses a plurality of serially connected piezoceramic nodes to form a distributed sensor capable of measuring waves generated in structures by damage events, including impact and crack propagation. Simulations show that the neural system can detect faint acoustic waves in large plates. An experiment demonstrates the use of a simple neural system that was able to measure simulated acoustic emissions that were not clearly recognizable by a single conventional piezoceramic sensor.

A continuous sensor for damage detection in bars

The concept of a continuous sensor for detecting vibration and stress waves in bars is investigated in this paper. This type of sensor is a long tape with a number of sensing nodes that are electrically connected together to form a single sensor with one channel of data output. The spacing of the sensor nodes can be designed such that the sensor is capable of detecting acoustic emissions (AEs) occurring at any point along the length of the sensor. An active fiber composite material or piezoceramic wafers can be used as the active element of the sensor. The characteristics of this new type of sensor were investigated by a simplified simulation and through experiments. The scope of the simulation is primarily to determine the characteristics of the new type of sensor that is proposed and the simulation is not intended to model the complex dispersion characteristics of guided waves in bars. The sensor was modeled as being integrated within a uniform cantilever bar to measure longitudinal stress wave propagation. Damping was included in the model to attenuate the wave as it travels. The sensor was connected to an electrical tuning circuit to examine the capability to filter out undesirable noise due to ambient vibration that would occur in practice. The elastic response of the bar was computed in closed form, and the coupled piezoelectric constitutive equations and electric circuit equations were solved using the Newmark-Beta numerical integration method. Strain, vibration, and wave propagation responses were simulated and results indicate that damage to the bar can be detected by recognizable changes in the sensor output as the wave propagates along the bar and passes over each sensor node. Experiments were performed to verify the concept of the continuous sensor on a composite bar. The testing showed the continuous sensor can replace four or more individual sensors to detect AEs and thus may be a practical method for structural health monitoring.

Monitoring Multi-Site Damage Growth During Quasi-Static Testing of a Wind Turbine Blade using a Structural Neural System:

Structural Health Monitoring (SHM) of a wind turbine blade using a Structural Neural System (SNS) is described in this paper. Wind turbine blades are composite structures with complex geometry and sections that are built of different materials. The 3D structure, large size, anisotropic material properties, and the potential for damage to occur anywhere on the blade makes damage detection a significant challenge. A SNS based on acoustic emission (AE) monitoring (passive listening) was developed for practical low cost SHM of large composite structures such as wind turbine blades. The SNS was tested to detect damage initiation and propagation on a 9 m long wind turbine blade during a quasi-static proof test to failure at the National Renewable Energy Laboratory test facility in Golden, Colorado. Twelve piezoelectric sensors were bonded on the surface of the wind turbine blade and connected to form four continuous sensors which were used in the SNS to determine damage locations. Although 12 sensors monitored the wind turbine blade, the SNS produces only two analog output signals; one time signal to determine and locate damage, and a second time signal containing combined AE waveforms. Testing of the wind turbine blade produced some interesting results. After initial emissions due to settling of the blade diminished, damage initiated at one location on the blade. As the load was increased, damage occurred in a sequence at three other locations until there was a catastrophic buckling failure of the blade. The buckling occurred above the design load for the blade, and was due to the carbon spar cap disbonding from the fiberglass shear web under compressive bending stress. The SNS indicated the general area where the damage started and how the damage progressed, which is valuable information for verifying and improving the blade design and the manufacturing procedure. Strain gages on the blade did not provide a clear indication of damage until buckling occurred. A major outcome of this testing was to provide confidence that SHM of large composite structures that have complex geometry and multiple materials is practical using a simple, low cost SNS.

Influence of attenuation on acoustic emission signals in carbon fiber reinforced polymer panels.

Influence of attenuation on acoustic emission (AE) signals in Carbon Fiber Reinforced Polymer (CFRP) crossply and quasi-isotropic panels is examined in this paper. Attenuation coefficients of the fundamental antisymmetric (A0) and symmetric (S0) wave modes were determined experimentally along different directions for the two types of CFRP panels. In the frequency range from 100 kHz to 500 kHz, the A0 mode undergoes significantly greater changes due to material related attenuation compared to the S0 mode. Moderate to strong changes in the attenuation levels were noted with propagation directions. Such mode and frequency dependent attenuation introduces major changes in the characteristics of AE signals depending on the position of the AE sensor relative to the source. Results from finite element simulations of a microscopic damage event in the composite laminates are used to illustrate attenuation related changes in modal and frequency components of AE signals.

Initial evaluation of an active/passive structural neural system for health monitoring of composite materials

Structural health monitoring is an underlying technology that can help to ensure safe operation and provide cost effective maintenance of advanced composite structures. While several general methods of health monitoring have evolved in recent years, there is still the goal of reducing the overall cost of applying health monitoring to large structures. Data acquisition hardware typically consumes most of the investment in a structural monitoring system. On a conventional system based on acoustic emission monitoring, a separate high sampling rate data acquisition channel is needed for each sensor to convert analog signals to digital signals to locate damage. Other methods of damage detection are likewise complicated, and require many sensors and actuators, auxiliary signal processing, and data storage instrumentation. This paper proposes a structural neural system that uses firing of sensor neurons to reduce the number of data acquisition channels needed for damage detection. The neural system can perform passive acoustic emission sensing or active wave propagation monitoring. A prototype structural neural system with four sensor inputs was built and tested, and experimental results are presented in the paper. One signal output from the structural neural system is used to predict the location of damage. A second signal provides the time domain response of the sensors. Therefore, passive and active health monitoring can be performed using two channels of data acquisition. The structural neural system significantly reduces the data acquisition hardware required for health monitoring, and combines some of the advantages that exist individually for passive and active health monitoring.

Experimental Damage Detection on a Wing Panel Using Vibration Deflection Shapes

This paper discusses the use of a vibration method to detect fatigue cracks in inaccessible internal structures. On aircraft, the lower wing panels are highly stressed causing cracks to initiate from fastener holes inside the wing box. The wing panel internal sections are usually inspected using conventional nondestructive inspection techniques after removing the wing panels from the wing box structure. When a crack is detected during the inspection, it can sometimes be repaired by reinforcing the damaged wing panel integral stiffener with a set of repair doublers. Of concern is whether the repair is intact for extended periods of aircraft service. The integrity of the repair and the condition of the wing interior structure might be determined by measuring the vibration of the outer surface. To investigate this approach, a series of experiments was conducted. Two piezoceramic actuator patches were bonded on the outside of a wing panel above a stiffener to generate vibration up to 80 kHz. A scanning laser doppler vibrometer was used to measure the normal vibration of the outside of the panel over the crack/repair. The measurements were performed for both the healthy and damaged panel. It was found that a crack extending from a fastener hole to the free edge of a stiffener and loosening of a repair doubler could be detected by changes in the vibration response of the outside of the panel. The crack from the fastener hole was not detectable until it reached the free edge of the stiffener.