Most modern railways use Continuous Welded Rail (CWR). A major problem is the almost total absence of expansion
joints that can create buckling in hot weather and breakage in cold weather due to the rail thermal stresses. In June 2008
the University of California, San Diego (UCSD), under the sponsorship of a Federal Railroad Administration (FRA)
Office of Research and Development (R&D) grant, began work to develop a technique for in-situ measurement of stress
and detection of incipient buckling in CWR. The method under investigation is based on ultrasonic guided waves, and
the ultimate goal is to develop a prototype that can be used in motion. A large-scale full rail track (70 feet in length) has
been constructed at UCSD's Powell Structural Laboratories, the largest laboratories in the country for structural testing,
to validate the CWR stress measurement and buckling detection technique under rail heating conditions well controlled
in the laboratory. This paper will report on the results obtained from this unique large-scale test track to date. These
results will pave the road for the future development of the rail stress measurement & buckling detection prototype.
The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research
and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype
has been designed and field tested with the support of Volpe National Transportation Systems Center and ENSCO, Inc.
The goal of this project is to develop a rail defect detection system that provides (a) better defect detection reliability
(including internal transverse head defects under shelling and vertical split head defects), and (b) higher inspection speed
than achievable by current rail inspection systems. This effort is also in direct response to Safety Recommendations
issued by the National Transportation Safety Board (NTSB) following the disastrous train derailments at Superior, WI in
1992 and Oneida, NY in 2007 among others. The UCSD prototype uses non-contact ultrasonic probing of the rail head
(laser and air-coupled), ultrasonic guided waves, and a proprietary real-time statistical analysis algorithm that maximizes
the sensitivity to defects while minimizing false positives. The current design allows potential inspection speeds up to 40
mph, although all field tests have been conducted up to 15 mph so far. This paper summarizes (a) the latest technology
development test conducted at the rail defect farm of Herzog, Inc. in St Joseph, MO in June 2010, and (b) the completion
of the new Rail Defect Farm facility at the UCSD Camp Elliott Field Station with partial in-kind donations from the
Burlington Northern Santa Fe (BNSF) Railway.
This paper presents numerical results on the dynamic behavior of continuously welded rails (CWR) subjected to a static
axial stress. The results quantify the sensitivity of guided waves to stress variations and could be potentially used to
estimate the stress level in CWR or alternatively the rail Neutral Temperature (stress free rail temperature). This work
represents the initial concept phase of a research and development study funded by the Federal Railroad Administration. The ultimate objective of this study is to develop and test a prototype system that uses non-contact dynamic sensing to measure in-situ rail stress in motion, to determine rail Neutral Temperatures (NT) and the related Incipient Buckling Risks in CWR.
This paper gives insight to the ultrasonic wave propagation in arbitrary cross section waveguides such as a rail. Due to
the geometrical complexity of the rail cross section, the analytical solution to the wave propagation in the rail is not
feasible. A Semi Analytical Finite Element method is described as an alternative yet still robust approach to get the
solution of the problem. The free-vibration solution (unforced) and the forced solution to a laser excitation, are shown
for the case of an undamped rail up to a frequency of 500 kHz. The effects of different loading patterns are discussed,
while experimental results are provided. A mode selection is performed, in accordance to the sensitivity of each mode to
the different types of defect that can occur in a rail.
Many bridges, including 90% of the California inventory, are post-tensioned box-girders concrete structures.
Prestressing tendons are the main load-carrying components of these and other post-tensioned structures. Despite their
criticality, much research is needed to develop and deploy techniques able to provide real-time information on the level
of prestress in order to detect dangerous stress losses. In collaboration with Caltrans, UCSD is investigating the
combination of ultrasonic guided waves and embedded sensors to provide both prestress level monitoring and defect
detection capabilities in concrete-embedded PS tendons.
This paper presents a technique based on nonlinear ultrasonic guided waves in the 100 kHz - 2 MHz range for
monitoring prestress levels in 7-wire PS tendons. The technique relies on the fact that an axial stress on the tendon
generates a proportional radial stress between adjacent wires (interwire stress). In turn, the interwire stress modulates
nonlinear effects in ultrasonic wave propagation through both the presence of finite strains and the interwire contact. The
nonlinear ultrasonic behavior of the tendon under changing levels of prestress is monitored by tracking higher-order
harmonics at (nω) arising under a fundamental guided-wave excitation at (ω). Experimental results will be presented to
identify (a) ranges of fundamental excitations at (ω) producing maximum nonlinear response, and (b) optimum lay-out of
the transmitting and the receiving transducers within the test tendons. Compared to alternative methods based on linear
ultrasonic features, the proposed nonlinear ultrasonic technique appears more sensitive to prestress levels and more
robust against changing excitation power at the transmitting transducer or changing transducer/tendon bond conditions.
Ultrasonic guided wave testing necessitates of quantitative, rather than qualitative, information on flaw size, shape
and position. This quantitative diagnosis ability can be used to provide meaningful data to a prognosis algorithm for
remaining life prediction, or simply to generate data sets for a statistical defect classification algorithm. Quantitative
diagnostics needs models able to represent the interaction of guided waves with various defect scenarios. One such
model is the Global-Local (GL) method, which uses a full finite element discretization of the region around a flaw to
properly represent wave diffraction, and a suitable set of wave functions to simulate regions away from the flaw.
Displacement and stress continuity conditions are imposed at the boundary between the global and the local regions.
In this paper the GL method is expanded to take advantage of the Semi-Analytical Finite Element (SAFE) method in
the global portion of the waveguide. The SAFE method is efficient because it only requires the discretization of the
cross-section of the waveguide to obtain the wave dispersion solutions and it can handle complex structures such as
multilayered sandwich panels. The GL method is applied to predicting quantitatively the interaction of guided waves
with defects in aluminum and composites structural components.
Nearly 90% of the bridges in California are post-tensioned box-girders. Prestressing (PS) tendons are the main load-carrying
components of these and other post-tensioned structures. Despite their criticality, much research is needed to
develop and deploy techniques able to provide real-time information on the level of prestress and on the presence of
structural defects (e.g. corrosion and broken wires) in the PS tendons. In collaboration with Caltrans, UCSD is
investigating the combination of ultrasonic guided waves and embedded sensors as an approach to provide both prestress
level monitoring and defect detection capabilities in concrete-embedded PS tendons.
This paper will focus on the prestress level monitoring by first discussing the behavior of ultrasonic guided waves
propagating in seven-wire, 0.6-in diameter twisted strands typically used in post-tensioned concrete structures. A semi-analytical
finite element analysis is used to predict modal and forced wave solutions as a function of the applied prestress
level. This analysis accounts for the changing inter-wire contact as a function of applied loads. A feature shown sensitive
to load levels is the inter-wire energy leakage. In order to monitor such feature, the method uses low-profile piezoelectric
sensors able to probe the individual, 0.2-in wires comprising the strand. Results of load monitoring in free and embedded
strands during laboratory tests will be presented.
Researchers at UCSD, with the initial support of NSF and the current support of the Federal Railroad
Administration (FRA), have been working on a flaw detection prototype for rails that uses non-contact ultrasonic
probing and robust data processing algorithms to provide high speed and high reliability defect detection in these structures. Besides the obvious advantages of non-contact probing, the prototype uses ultrasonic guided waves able to detect and quantify transverse cracks in the rail head, notoriously the most dangerous of all rail track defects. This paper will report on the first field test which was conducted in Gettysburg, PA in March 2006 with the technical support of ENSCO, Inc. Good results were obtained for the detection of both surface-breaking and internal cracks ranging in size from 2% cross-sectional head area (H.A.) reduction to 80% H.A. reduction.
Recent train accidents and associated direct and indirect costs including cost of repair of equipment and infrastructure as well as delay and death/injury costs, have reaffirmed the need for developing rail defect detection systems more effective than those used today. In fact, rail defect detection has been identified as a priority area in the U.S. Federal Railroad Administration 5-year R&D plan. This paper proposes an unsupervised learning algorithm for defect detection in rails. The algorithm is used in a non-contact inspection system that is targeted to the detection of transverse-type cracks in the rail head (including transverse fissures and detail fractures), notoriously the most dangerous flaws in rails. The system uses ultrasonic guided waves that are generated by a pulsed laser and are detected by air-coupled sensors positioned as far away as 76 mm (3") from the top of rail head. The inspection ranges is at least 500 mm (20") for surface head cracks as shallow as 1 mm. Fast data output is achieved by processing the ultrasonic defect signatures by Wavelet Transform algorithms. The features extracted after wavelet processing are analyzed by a learning algorithm based on novelty detection. This algorithm attempts to detect the presence of damage despite the normal variations in ultrasonic signal features that may be found in a field test.
The monitoring of adhesively-bonded joints through the use of ultrasonic guided waves is the general topic of this paper. Specifically, composite-to-composite joints representative of the wing skin-to-spar bonds of Unmanned Aerial Vehicles (UAVs) are examined. This research is the first step towards the development of an on-board structural health monitoring system for UAV wings based on integrated ultrasonic sensors. The study investigates two different lay-ups for the wing skin and two different types of bond defects, namely poorly-cured adhesive and disbonded interfaces. The guided wave propagation problem is studied numerically by a semi-analytical finite element method that accounts for viscoelastic damping, and experimentally by utilizing macro fiber composite (MFC) transducers which are inexpensive, flexible, highly robust, and viable candidates for application in on-board monitoring systems. Based upon change in energy transmission, the presence of damage is successfully identified through features extracted in both the time domain and discrete wavelet transform domain. A unique "passive" version of the diagnostic system is also demonstrated experimentally, whereby MFC sensors are utilized for detecting and locating simulated active damage in an aluminum plate. By exploiting the directivity behavior of MFC sensors, a damage location algorithm which is independent of wave speed is developed. Application of this approach in CFRP components may alleviate difficulties associated with damage location in highly anisotropic systems.
KEYWORDS: Ferroelectric materials, Sensors, Transducers, Structural health monitoring, Wave propagation, Diagnostics, Microsoft Foundation Class Library, Aluminum, Active sensors, Actuators
This paper presents a self-diagnostic and sensor validation procedure that performs in-situ monitoring of the operational status of piezoelectric (PZT) sensors and actuators used in structural health monitoring (SHM) applications. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the SHM process with large numbers of active sensors typically deployed in a structure. Both degradation of the mechanical/electrical properties of a PZT transducer and the bonding defects between a PZT patch and a host structure could be identified using the proposed procedure. First, the effects of bonding defects between a PZT patch and a host structure on high frequency SHM techniques, including Lamb wave propagations and impedance methods, have been experimentally investigated. It has been found that the effects are remarkable, modifying wave phase and amplitude, creating new wave modes, and changing measured impedance spectrum. These changes can lead to the false indications on structural conditions without an efficient sensor-diagnostic procedure. The feasibility of the proposed sensor diagnostics procedure was then demonstrated by analytical studies and experimental examples, where the functionality of surface-mounted piezoelectric sensors was continuously deteriorated. The proposed procedure can provide a metric that can be used to determine the sensor functionality over a long period of service time or after an extreme loading event. Further, the proposed procedure can be useful if one needs to check the operational status of a sensing network right after its installation.
Unmanned Aerial Vehicles (UAVs) are being increasingly used in military as well as civil applications. A critical part of the structure is the adhesive bond between the wing skin and the supporting spar. If not detected early, bond defects originating during manufacturing or in service flight can lead to inefficient flight performance and eventual global failure. This paper will present results from a bond inspection system based on attached piezoelectric disks probing the skin-to-spar bondline with ultrasonic guided waves in the hundreds of kilohertz range. The test components were CFRP composite panels of two different fiber layups bonded to a CFRP composite tube using epoxy adhesive. Three types of bond conditions were simulated, namely regions of poor cohesive strength, regions with localized disbonds and well bonded regions. The root mean square and variance of the received time-domain signals and their discrete wavelet decompositions were computed for the dominant modes propagating through the various bond regions in two different inspection configurations. Semi-analytical finite element analysis of the bonded multilayer joint was also carried out to identify and predict the sensitivity of the predominant carrier modes to the different bond defects. Emphasis of this research is based upon designing a built-in system for monitoring the structural integrity of bonded joints in UAVs and other aerospace structures.
Recent train accidents and associated direct and indirect repair costs have reaffirmed the need for developing rail defect detection systems more effective than those used today. The group at the UCSD NDE & Structural Health Monitoring Laboratory, in collaboration with the US Federal Railroad Administration, is conducting a study that aims at developing an inspection strategy for rails based on guided ultrasonic waves. This paper illustrates a guided-wave inspection system that is targeted to the detection of transverse-type cracks in the rail head, that are among the most dangerous flaws in rails. The methodology is based on a hybrid non-contact system that uses a pulsed laser for generating waves and multiple air-coupled sensors for detecting waves. The remote sensors are positioned as far away as 76 mm (3”) from the top of rail head. Signal processing based on the Continuous Wavelet Transform is used to characterize the time-frequency content of the propagating waves. Features extracted after Discrete Wavelet processing of the wave signals result in a damage index that is robust with respect to noise and is related to the crack depth; the method allows for fast inspection with the potential for quantifying the extent of the flaw. It is demonstrated that the adopted setup allows for the detection of small cracks, as shallow as 1 mm in depth. It is also shown that the ultrasonic wave features considered in this study are directly related to the reduction of the rail head cross-sectional area caused by a transverse crack.
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