Acoustic emission (AE) has been recognized for its unique capabilities as an NDT method. However, there is untapped
potential for the practical application of AE to structural health monitoring and prognosis. As part of the development of a wireless sensor network for structural bridge health monitoring, this study aims to provide a framework for the estimation of fatigue damage and remaining life of steel bridge components through AE monitoring. Fourteen compact tension (CT) specimens and nine cruciform fillet welded joints were used in AE monitored fatigue tests to investigate the correlation of AE features with crack growth in base materials and weldments. The material (structural steel A572 Grade 50) and the welding procedures are representative of those used in actual bridge construction. Based on the balance between AE signal energy and the energy release due to crack growth, deterministic models are presented to predict crack extension and remaining fatigue life for stable and unstable crack stages. The effect of weld length and fatigue load ratio on the AE activity is evaluated. The presence of noise is inevitable in the application of AE monitoring. The efficiency of data filtering and reduction algorithms is key to minimize the power and data storage demand of the wireless sensing system. AE data filtering protocols based on load pattern, source location, waveform feature analysis, and pattern recognition are proposed to minimize noise-induced AE and false indications due to wave reflections.
KEYWORDS: Transducers, Sensors, Bridges, Active remote sensing, Wave propagation, Passive remote sensing, Semiconducting wafers, Active sensors, Signal to noise ratio, Signal detection
Monitoring of fatigue cracking in bridges using a combined passive and active scheme has
been approached by the authors. Passive Acoustic Emission (AE) monitoring has shown to be able to
detect crack growth behavior by picking up the stress waves resulting from the breathing of cracks while
active ultrasonic pulsing can quantitatively assess structural integrity by sensing out an interrogating
pulse and receive the structural reflections from the discontinuity. In this paper, we present a
comparative study of active and passive sensing with two types of transducers: (a) AE transducers, and
(b) embeddable piezoelectric wafer active sensors (PWAS). The study was performed experimentally on
steel plates. Both pristine and damaged (notched) conditions were considered. For active sensing, pitchcatch
configuration was examined in which one transducer was the transmitter and another transducer
acted as the receiver. The ping signal was generated by the AE hardware/software package AEwin. For
passive sensing, 0.5-mm lead breaks were executed both on top and on the edge of the plate. The
comparative nature of the study was achieved by having the AE and PWAS transducers placed on the
same location but on the opposite sides of the plate. The paper presents the main findings of this study
in terms of (a) signal strength; (b) signal-to-noise (S/N) ratio; (c) waveform clarity; (d) waveform
Fourier spectrum contents and bandwidth; (e) capability to detect and localize AE source; (f) capability
to detect and localize damage. The paper performs a critical discussion of the two sensing
methodologies, conventional AE transducers vs. PWAS transducers.
Signal identification including noise filtering and reduction of acquired signals is needed to achieve efficient and
accurate data interpretation for remote acoustic emission (AE) monitoring of in-service steel bridges. Noise filtering may
ensure that genuine hits from crack growth are involved in the estimation of fatigue damage and remaining fatigue life.
Reduction of the data quantity is desirable for the sensing system to conserve energy in the data transmission and
processing procedures. Identification and categorization of acquired signals is a promising approach to effectively filter
and reduce AE data in the application of bridge monitoring. In this study an investigation on waveform features (time
domain and frequency domain) and relevant filters is carried out using the results from AE monitored fatigue tests. It is
verified that duration-amplitude (D-A) filters are effective to discriminate against noise for results of steel fatigue tests.
The study is helpful to find an appropriate AE data filtering protocol for field implementations.
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.
Acoustic emission (AE) monitoring is desirable to nondestructively detect fatigue damage in steel bridges. Investigations
of the relationship between AE signals and crack growth behavior are of paramount importance prior to the widespread
application of passive piezoelectric sensing for monitoring of fatigue crack propagation in steel bridges. Tests have been
performed to detect AE from fatigue cracks in A572G50 steel. Noise induced AE signals were filtered based on friction
emission tests, loading pattern, and a combined approach involving Swansong II filters and investigation of waveforms.
The filtering methods based on friction emission tests and load pattern are of interest to the field evaluation using sparse
datasets. The combined approach is suitable for data filtering and interpretation of actual field tests. The pattern
recognition program NOESIS (Envirocoustics) was utilized for the evaluation of AE data quality. AE parameters are
associated with crack length, crack growth rate, maximum stress intensity and stress intensity range. It is shown that AE
hits, counts, absolute energy, and signal strength are able to provide warnings at the critical cracking level where
cracking progresses from stage II (stable propagation) to stage III (unstable propagation which may result in failure).
Absolute energy rate and signal strength rate may be better than count rate to assess the remaining fatigue life of inservice
steel bridges.
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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