Large format additive manufacturing (LFAM) proved to have a great potential to become an adjacent technology to traditional manufacturing methods. One of the sectors LFAM is targeting is rapid tool/mold development for composites. This includes large mold structures used for high-temperature molding techniques (in-oven or autoclave). Although, these large printed structures (reaching hundreds of pounds) develop thermal-residual stress during cool-down and can eventually crack, turning the structure into waste. Acoustic emission (AE), a passive non-intrusive global nondestructive evaluation (NDE) technique, was used to monitor crack growth and can provide the right tools that can be used for feedback loop for corrective action. This research performs thermal testing on a large AM mold with preexisting cracks, in an attempt to monitor crack growth using AE. AE was able to detect, identify and locate the crack source by means of acoustic features, waveform characteristics, spectrum analysis, and difference in arrival times.
Acoustic tomography method facilitates mapping internal defects in real-time and in-situ without destructive testing. The method requires certain number of transmitter and receiver paths to reconstruct the slowness map of scanned area depending upon the target resolution. Once the hardware component is determined, the major software output to feed into the algorithm is the time of flight. There are sophisticated signal processing methods reported in literature to determine the time of flight (TOF) with better accuracy as compared to conventional threshold-based method. The most common approaches are wavelet-based or energy-based methods, which require transforming time history signal into different domains. Domain transformation is typically applied in laboratory-scale experiments. In this paper, a new arrival time pick-up approach based on defining outliers in the derivative of transient signal in time domain is evaluated in terms of accuracy, computational effort and power as compared to threshold-based and wavelet/energy-based methods reported in literature. The waveforms from experiments is used to study the influence of materials and signal-to-noise ratio on the accuracy of detecting the fastest wave mode. In addition, waveforms are also artificially generated with fixed wave velocity using numerical models to further evaluate the performance of the different methods (outlier-based, threshold-based and energy-based). The influence of tomography quality by using these to this method performs better in accuracy and efficiency.
KEYWORDS: Structural health monitoring, Sensors, Bridges, Data transmission, Data acquisition, Composites, Acoustic emission, Signal processing, Signal analyzers, Electronics
This paper discusses the development of an Acoustic Emission (AE) wireless node and its application for SHM (Structural Health Monitoring). The instrument development was planned for applications monitoring steel and concrete bridges components. The final product, now commercially available, is a sensor node which includes multiple sensing elements, on board signal processing and analysis capabilities, signal conditioning electronics, power management circuits, wireless data transmission element and energy harvesting unit. The sensing elements are capable of functioning in both passive and active modes, while the multiple parametric inputs are available for connecting various sensor types to measure external characteristics affecting the performance of the structure under monitoring. The output of all these sensors are combined and analyzed at the node in order to minimize the data transmission rate, which consumes significant amount of power. Power management circuits are used to reduce the data collection intervals through selective data acquisition strategies and minimize the sensor node power consumption. This instrument, known as the 1284, is an excellent platform to deploy SHM in the original bridge applications, but initial prototypes has shown significant potential in monitoring composite wind turbine blades and composites mockups of Unmanned Autonomous Vehicles (UAV) components; currently we are working to extend the use of this system to fields such as coal flow, power transformer, and off-shore platform monitoring.
Combustion turbine components operate under extreme environmental conditions and are susceptible to failure. Turbine blades are the most susceptible components and need to be regularly inspected to assure their integrity. Undetected cracks on these blades may grow quickly due to the high fatigue loading to which they are subjected and eventually fail causing extensive damage to the turbine. Cracks in turbine blades can originate from manufacturing errors, impact damages or the due to corrosion from the aggressive environment in which they operate. The component most susceptible to failure in a combustion turbine is the mid-compressor blades. In this region, the blades experience the highest gradients in temperature and pressure. Cracks in the rotator blades can be detected by vibration monitoring; while, the stator vanes or blades cracking can only be monitored by Acoustic Emission (AE) method. The stator vanes are in contact with the external casing of the turbine and therefore, any acoustic emission activity from the blades can be captured non-intrusively by placing sensors on the turbine casing. The acoustic emission activity from cracks that are under fatigue loading is significantly higher than the background noise and hence can be captured and located accurately by a group of AE sensors. Using a total of twelve AE sensors
per turbine, the crack generation and propagation in the stator vanes of the mid-compressor section is monitored continuously. The cracks appearing in the stator vanes is clearly identified and located by the AE sensors.
This work deals with the non destructive analysis of different composite parts and structures using Line Scanning
Thermography (LST), a non-contact inspection method based in dynamic thermography. The LST technique provides a
quick and efficient methodology to scan wide areas rapidly; the technique has been used on the inspection of composite
propellers, sandwich panels, motor case tubes and wind turbine blades, among others.
In LST a line heat source is used to thermally excite the surface under study while an infrared detector records the
transient surface temperature variation of the heated region. Line Scanning Thermography (LST), has successfully been
applied to determine the thickness of metallic plates and to assess boiler tube thinning.
In this paper the LST protocols developed for the detection of sub-surface defects in different composite materials
commonly used in aerospace applications, plates will be presented. In most cases the thermal images acquired using LST
will be compared with ultrasonic c-scans. The fundamentals of LST will be discussed, as well as the limitations of this
technique for NDT inspection.
This paper discusses the development status of a self-powered wireless sensor node for steel and concrete bridges
monitoring and prognosis. By the end of the third year in this four-year cross-disciplinary project, the 4-channel acoustic
emission wireless node, developed by Mistras Group Inc, has already been deployed in concrete structures by the
University of Miami. Also, extensive testing is underway with the node powered by structural vibration and wind energy
harvesting modules developed by Virginia Tech. The development of diagnosis tools and models for bridge prognosis,
which will be discussed in the paper, continues and the diagnosis tools are expected to be programmed in the node's
AVR during the 4th year of the project. The impact of this development extends beyond the area of bridge health
monitoring into several fields, such as offshore oil platforms, composite components on military ships and race boats,
combat deployable bridges and wind turbine blades. Some of these applications will also be discussed. This project was
awarded to a joint venture formed by Mistras Group Inc, Virginia Tech, University of South Carolina and University of
Miami by the National Institute of Standards and Technology through its Technology Innovation Program Grant
#70NANB9H007.
Today, the increasing energy demand and the need for clean power generation has lead to the
improvement of wind turbines and the development non invasive inspection techniques for the
assessment of wind turbine blades to maintain long term reliability as well as to avoid catastrophic
failures.
Given the complexity of the geometry, the material composition and material thicknesses, finding a
NDT technique to effectively and rapidly inspect the blades is a challenging task. Wind turbine
blades are fabricated using different materials like fiber glass, carbon composites, foam and/ or balsa
wood. Layers of these materials are bonded together using an epoxy type resin. Inspection of the
bond quality between external layers and structural elements of the blade is of fundamental
importance for quality control and service of the blade.
In this study our efforts towards the applications of Line Scanning Thermography (LST) for the
analysis of test coupons fabricated using the materials employed in the manufacture of wind turbine
blades, as well as some wind turbine blade sections. LST utilizes a line heat source to thermally
excite the surface to be inspected and an infrared detector to record the transient surface temperature
variation produced by disbonds, and other subsurface imperfections. The LST technique has
provided a quick and efficient methodology to scan large composite structures, which makes it
desirable for the inspection of wind turbine blades. The scanning protocols developed for the
detection of sub-surface disbonds (delamination) in coupons and parts will be presented. The
successes and limitations of the technique will be discussed.
KEYWORDS: Sensors, Acoustic emission, Transducers, Signal detection, Bridges, Aluminum, Signal to noise ratio, Active sensors, Semiconducting wafers, Interference (communication)
Piezoelectric wafer active sensors (PWAS) are non-intrusive transducers that can convert
mechanical energy into electrical energy, and vice versa. They are well known for their dual use as either
actuators or sensors. Though PWAS has shown great potential for active sensing, its capability for
acoustic emission (AE) detection has not yet been exploited. In the reported work, we have explored the
implementation of PWAS transducers for both passive (AE sensors) and active (in-situ ultrasonic
transducers) sensing using a single PWAS network. The objective of the work presented in this paper is to
adapt PWAS as AE sensors and compare it to the commercially available AE transducers such as PAC
R15.
An experiment has been designed to show how PWAS can be used for AE detection and the results
were compared to a standard AE sensor, PAC R15I. Tests on compact tension specimens have also been
conducted to show PWAS capability to pick up AE events during fatigue loading. PWAS field
installation technology has been tested with packaging similar to that used for traditional strain gauges.
The performance of packaged PWAS has been compared with that of conventional AE transducers R15I.
We have found that PWAS not only can detect the presence of AE events but also can provide a wide
frequency bandwidth. At this stage, PWAS underperforms the commercial AE sensors. To make PWAS
ready for field test, signal to noise ratio needs to be significantly improved.
This paper presents the most recent advances in the development of a self powered wireless sensor network for steel and
concrete bridges monitoring and prognosis. This five-year cross-disciplinary project includes development and
deployment of a 4-channel acoustic emission wireless node powered by structural vibration and wind energy harvesting
modules. In order to accomplish this ambitious goal, the project includes a series of tasks that encompassed a variety of
developments such as ultra low power AE systems, energy harvester hardware and especial sensors for passive and
active acoustic wave detection. Key studies on acoustic emission produced by corrosion on reinforced concrete and by
crack propagation on steel components to develop diagnosis tools and models for bridge prognosis are also a part of the
project activities. It is important to mention that the impact of this project extends beyond the area of bridge health
monitoring. Several wireless prototype nodes have been already requested for applications on offshore oil platforms,
composite ships, combat deployable bridges and wind turbines. This project was awarded to a joint venture formed by
Mistras Group Inc, Virginia Tech, University of South Carolina and University of Miami and is sponsored through the
NIST-TIP Grant #70NANB9H007.
An innovative Line Scanning Thermography (LST) inspection method is being developed as part of a
Structural Damage Assessment System to access the health of in-service composite structures. The system
utilizes a line heat source to thermally excite the surface inspected and an infrared detector to record the
transient surface temperature variation and to detect regions of increased heat resistance associated to
interlaminar disbonds, cracks and other imperfections found in composites structures. In this study our efforts
towards the applications of LST for the analysis of carbon fiber sandwich composites will be discussed. The
LST technique provides a quick and efficient methodology to scan wide areas rapidly. The scanning protocols developed for the detection of sub-surface disbonds (delamination) in composite sandwich parts will be presented. The results presented correspond to scans of test coupons with manufactured defects.
KEYWORDS: Sensors, Composites, Acoustic emission, Photogrammetry, Signal generators, Nondestructive evaluation, Inspection, Picture Archiving and Communication System, Data acquisition boards, Research facilities
Acoustic emission (AE) was monitored in notched full-scale honeycomb sandwich composite curved fuselage panels
during loading. The purpose of the study was to evaluate the AE technique as a tool for detecting notch tip damage
initiation and evaluating damage severity in such structures. This evaluation was a part of a more general study on the
damage tolerance of six honeycomb sandwich composite curved panels, each containing a different damage scenario.
The overall program objective was to investigate the effects of holes and notches on residual strength. The investigation
was conducted using the Full-Scale Aircraft Structural Test Evaluation and Research (FASTER) facility located at the
Federal Aviation Administration William J. Hughes Technical Center, Atlantic City International Airport, NJ. This
paper reports on the AE results recorded during the loading to failure of two selected panels. The results show that
damage initiation at the tips of the notches, and its progression along the panel, could be detected and located. These AE
results were correlated with the deformation and strain fields measured through strain photogrammetry, throughout
loading, at the vicinity of these notches. This correlation aided in interpreting the AE results. While the fretting among
the newly created fracture surfaces generated a large number of
low-intensity AE signals, the high-intensity signals
generated at high load levels provided a good measure for anticipating incipient fracture. Further, the AE results located
internal disbonding caused during panel fabrication. The large number of low-intensity AE signals generated from the
disbonded regions was associated with the fretting among the disbonded surfaces.
Diaphragm-type couplings are high misalignment torque and speed transfer components used in aircrafts. Crack
development in such couplings, or in the drive train in general, can lead to component failure that can bring down an
aircraft. Real time detection of crack formation and growth is important to prevent such catastrophic failures. However,
there is no single Nondestructive Monitoring method available that is capable of assessing the early stages of crack
growth in such components. While vibration based damage identification techniques are used, they cannot detect cracks
until they reach a considerable size, which makes detection of the onset of cracking extremely difficult. Acoustic
Emission (AE) can detect and monitor early stage crack growth, however excessive background noise can mask acoustic
emissions produced by crack initiation. Fusion of the two mentioned techniques can increase the accuracy of
measurement and minimize false alarms. However, a monitoring system combining both techniques could prove too
large and heavy for the already restricted space available in aircrafts.
In the present work, we will present a newly developed integrated Acoustic Emission/Vibration (AE/VIB) combined
sensor which can operate in the temperature range of -55°F to 257°F and in high EMI environment. This robust AE/VIB
sensor has a frequency range of 5 Hz-2 kHz for the vibration component and a range of 200-400 kHz for the acoustic
emission component. The sensor weight is comparable to accelerometers currently used in flying aircraft. Traditional
signal processing approaches are not effective due to high signal attenuation and strong background noise conditions,
commonly found in aircraft drive train systems. As an alternative, we will introduce a new Supervised Pattern
Recognition (SPR) methodology that allows for simultaneous processing of the signals detected by the AE/VIB sensor
and their classification in near-real time, even in these adverse conditions. Finally, we will discuss the architecture
developed to produce a fully autonomous monitoring tool based on the fusion of the AE and Vibration techniques.
This paper presents the research and development of an Acoustic Health Monitoring (AHM) system that uses Guided Lamb Wave (GLW) technology to determine the thickness of railroad tank car shells for identification of wall loss due to corrosion. In recent regulatory changes, the emphasis has shifted from the traditional hydrotest to more modern methods for assuring tank car integrity. The new generation of maintenance programs will rely heavily on nondestructive testing, and will use damage tolerance concepts and risk analysis to establish inspection frequencies and items to inspect. It is the responsibility of the owners to set up experience-based maintenance programs that are suitable for the working conditions of their own particular fleets. Development of an ideal AHM system for railroad cars would be an instrument that incorporates Acoustic Emission (AE) and GLW technology. The combination of active and passive acoustic technologies integrated into a single system would be a highly efficient means of determining the structural integrity of tank cars. The integration of the GLW technology will allow identification of corrosion wall loss in a zone between two sensors, rather than at a single point (traditional ultrasonic thickness measurements). Thus, a much larger area of the structure can be inspected for approximately the same inspection cost. With a suitable integration of this new technology into the overall inspection and corrosion management program, the fleet can be more efficiently maintained and the risk of accidental release through progressive corrosion damage can be significantly reduced.
KEYWORDS: Metals, Sensors, Acoustic emission, Signal processing, Software development, Neural networks, Warfare, Picture Archiving and Communication System, Control systems, Data acquisition
This paper presents the latest results obtained from Acoustic Emission (AE) monitoring and detection of cracks and/or damage in diaphragm couplings, which are used in some aircraft and engine drive systems. Early detection of mechanical failure in aircraft drive train components is a key safety and economical issue with both military and civil sectors of aviation. One of these components is the diaphragm-type coupling, which has been evaluated as the ideal drive coupling for many application requirements such as high speed, high torque, and non-lubrication. Its flexible axial and angular displacement capabilities have made it indispensable for aircraft drive systems. However, diaphragm-type couplings may develop cracks during their operation. The ability to monitor, detect, identify, and isolate coupling cracks on an operational aircraft system is required in order to provide sufficient advance warning to preclude catastrophic failure. It is known that metallic structures generate characteristic Acoustic Emission (AE) during crack growth/propagation cycles. This phenomenon makes AE very attractive among various monitoring techniques for fault detection in diaphragm-type couplings. However, commercially available systems capable of automatic discrimination between signals from crack growth and normal mechanical noise are not readily available. Positive classification of signals requires experienced personnel and post-test data analysis, which tend to be a time-consuming, laborious, and expensive process. With further development of automated classifiers, AE can become a fully autonomous fault detection technique requiring no human intervention after implementation. AE has the potential to be fully integrated with automated query and response mechanisms for system/process monitoring and control.
KEYWORDS: Principal component analysis, Acoustic emission, Picture Archiving and Communication System, Pattern recognition, Statistical analysis, Manufacturing, Data acquisition, Signal generators, Control systems, Inspection
In order to develop an effective and accurate way to monitor and control the quality of fiberglass products, Acoustic Emission (AE) signals, generated during compression of fiberglass samples, were studied and analyzed using neural network based pattern recognition software. Distinguishable patterns were found in samples manufactured under different conditions and compositions, which resulted in different product quality. AE waveform features, such as absolute energy, average frequency, duration, and rise time were analyzed and the features showed strong dependence on the sample tested. This made sample classification possible and definitive and therefore a classifier was developed and applied to data collected from additional test samples. Finally, an AE system for the evaluation of fiberglass insulation was designed and built. It is expected that the developed system will be used as a quality control tool in industrial production of fiberglass insulating material. In this paper we will discuss the AE data collection and analysis, classifier development, and give an overview of the inspection system developed.
This paper presents the results obtained during the development of a semi-real-time monitoring methodology based on Neural Network Pattern Recognition of Acoustic Emission (AE) signals for early detection of cracks in couplings used in aircraft and engine drive systems. AE signals were collected in order to establish a baseline of a gear-testing fixture background noise and its variations due to rotational speed and torque. Also, simulated cracking signals immersed in background noise were collected. EDM notches were machined in the driving gear and the load on the gearbox was increased until damaged was induced. Using these data, a Neural Network Signal Classifier (NNSC) was implemented and tested. The testing showed that the NNSC was capable of correctly identifying six different classes of AE signals corresponding to different gearbox operation conditions. Also, a semi-real-time classification software was implemented. This software includes functions that allow the user to view and classify AE data from a dynamic process as they are recorded at programmable time intervals. The software is capable of monitoring periodic statistics of AE data, which can be used as an indicator of damage presence and severity in a dynamic system. The semi-real-time classification software was successfully tested in situations where a delay of 10 seconds between data acquisition and classification was achieved with a hit rate of 50 hits/second per channel on eight active AE channels.
Many of today's methods of inspecting structures are very time consuming, labor intensive and in many cases (due to limited access), impractical. In addition, long shutdown times are required to perform the inspections, thus creating tremendous expenses associated with manpower, materials and lost production. With continuing advances in signal processing and communications a significant interest has been shown in developing new diagnostic technologies for monitoring the integrity of structures with known defects, or for detecting new defects, in real time with minimum human involvement. The continued use of aging structures, especially in regard to the airworthiness of aging aircraft, is a major area of concern. Recent developments in both active and passive Acoustic Emission monitoring as an advanced tool for 'Structural Health Management Systems (SHMS),' are illustrated by using two recently developed acoustic emission systems; the Acoustic Emission-Health and Usage Monitoring System (AE-HUMS) helicopter drivetrain health monitoring system, and the Acoustic Emission Flight Instrument System (AEFIS) composite health monitoring system. The data collected with these types of systems is processed with advanced data screening and classification techniques, which are employed to take full advantage of parametric and waveform-based acoustic emission.
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