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.
KEYWORDS: Acoustic emission, Electronic filtering, Mechanics, Sensors, Data modeling, Transducers, Data acquisition, Linear filtering, Monte Carlo methods, Digital filtering
This paper compares six different filtering protocols used in Acoustic Emission (AE) monitoring of fatigue crack
growth. The filtering protocols are combination of three different filtering techniques which are based on Swansong-like
filters and load filters. The filters are compared deterministically and probabilistically. The deterministic
comparison is based on the coefficient of determination of the resulting AE data, while the probabilistic comparison
is based on the quantification of the uncertainty of the different filtering protocols. The uncertainty of the filtering
protocols is quantified by calculating the entropy of the probability distribution of some AE and fracture mechanics
parameters for the given filtering protocol. The methodology is useful in cases where several filtering protocols are
available and there is no reason to choose one over the others. Acoustic Emission data from a compact tension
specimen tested under cyclic load is used for the comparison.
KEYWORDS: Data modeling, Acoustic emission, Nondestructive evaluation, Composites, Aerospace engineering, Homeland security, Current controlled current source, Bayesian inference, Monte Carlo methods
Acoustic emission (AE) is generated when cracks develop and it is used as an indicator of the current state of
damage in structural elements. Algorithms that use AE data to predict the state of a structural element are still in
their research stages because the relationship between crack length and AE activity is not well understood. The
process of trying to predict the future stage of a crack based on AE data is usually performed by an expert, and
requires significant experience. This paper proposes a new strategy for the use of AE data for structural prognosis.
A probabilistic model is used to predict AE data. An expert can analyze this data to draw conclusions about the
health of the structural member. The goal is to aid the analyst by providing an estimation of the AE activity in the
future. The methodology provides the cumulative signal strength at a future number of cycles, assuming the loading
and boundary conditions hold. The methodology uses a relationship between the rate of change of the cumulative
absolute energy of the AE with respect to the number of cycles and the stress intensity range. A third order
polynomial equation that describes the stress intensity range as function of the AE data is proposed. The variables
to be updated are treated as random and their joint probability distribution is computed using Bayesian inference.
Markov Chain Monte Carlo (MCMC) is used to forecast the cumulative signal strength at some number of cycles in
the future. The methodology is tested using a compact test specimen tested in structures lab at the University of
South Carolina.
KEYWORDS: Acoustic emission, Data modeling, Active remote sensing, Bridges, Transducers, Sensors, Lead, Signal to noise ratio, Genetic algorithms, Signal detection
Monitoring of fatigue cracks in steel bridges is of interest to bridge owners and agencies. Monitoring of fatigue cracks
has been attempted with acoustic emission using either resonant or broadband sensors. One drawback of passive sensing
is that the data is limited to that caused by growing cracks. In this work, passive emission was complemented with
active sensing (piezoelectric wafer active sensors) for enhanced detection capabilities. Passive and active sensing
methods were described for fatigue crack monitoring on specialized compact tension specimens. The characteristics of
acoustic emission were obtained to understand the correlation of acoustic emission behavior and crack growth. Crack
and noise induced signals were interpreted through Swansong II Filter and waveform-based approaches, which are
appropriate for data interpretation of field tests. Upon detection of crack extension, active sensing was activated to
measure the crack size. Model updating techniques were employed to minimize the difference between the numerical
results and experimental data. The long term objective of this research is to develop an in-service prognostic system to monitor structural health and to assess the remaining fatigue life.
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