This paper is concerned with the detection of physical flaws on pipe walls in gas pipelines. The sensor technology is EMAT, a non-contact ultrasonic technology. One EMAT is used as a transmitter, exciting an ultrasonic impulse into the pipe wall. Another EMAT located a few inches away from the first is used as a receiving transducer. This paper reports on the identification of flaw signatures in the receiver output. The first step in flaw characterization is to perform wavelet analysis of the signature. Being non-shift-invariant, an array of coefficients of a discrete wavelet transfor of a signal is not directly suitable as a pattern recognition feature. However, comparing composite properties of the signal on different scales is useful, because the more conversion caused by a flaw, changes the composite properties of the signal in wavelet space. For EMAT data, the useful information projects onto five mutually orthogonal wavelet scales. This paper reports onteh use of a robust 17-dimensional feature vector that mutually orthogonal wavelet scales. This paper reports on the use of a robust 17-dimensional featuer vector that consistently distinguishes "flaw" signatures from "no-flaw" signatures in a substantial collection of experimental data.
At its substratum, brain/mind organization requires both synaptic firings and non-synaptic events. Synaptic firings organize the pattern of non-synaptic events. Non-synaptic events organize the pattern of synaptic firings. The processes are related in a bizarre hierarchy. Comparing these processes to electric circuits, it is as if we have two circuits that each continuously and simultaneously update the topology, and consequently, the dynamical laws of the other. Since either can be seen to be rebuilding the other, from its own perspective each process appears higher than the other in a hierarchy. This same kind of hierarchy is found in a hyperset structure. Interpreted as a directed graph, the nodes in a hyperset form a hierarchy in which, from the perspective of any node in the hierarchy, that node is at the top. This organizational structure violates the Foundation Axiom. Algorithmic computation strictly complies with the Foundation Axiom. Thus, an algorithm organized like a hyperset is a contradiction in terms. Does this contradiction mean are we precluded forever from implementing brain-like activities artificially? Not at all! An algorithm is incapable of doing the job, but nothing prevents us from constructing interacting analog processes that update each other's dynamical laws on the fly.
As U.S. natural gas supply pipelines are aging, non-destructive inspection techniques are needed to maintain the integrity and reliability of the natural gas supply infrastructure. Ultrasonic waves are one promising method for non-destructive inspection of pipeline integrity. As the waves travel through the pipe wall, they are affected by the features they encounter. In order to build a practical inspection system that uses ultrasonic waves, an analysis method is needed that can distinguish between normal pipe wall features, such as welds, and potentially serious flaws, such as cracks and corrosion. Ideally, the determination between “flaw” and “no-flaw” must be made in real-time as the inspection system passes through the pipe. Because wavelet basis functions share some common traits with ultrasonic waves, wavelet analysis is particularly well-suited for this application. Using relatively simple features derived from the wavelet analysis of ultrasonic wave signatures traveling in a pipe wall, we have successfully demonstrated the ability to distinguish between the “flaw” and “no-flaw” classes of ultrasonic features.
Laser-based ultrasonic (LBU) measurement shows great promise for on-line monitoring of weld quality in tailor-welded blanks. Tailor-welded blanks are steel blanks made from plates of different thickness and/or properties butt-welded together; they are used in automobile manufacturing to provide body, frame, and closure panels. LBU uses a pulsed laser to generate the ultrasound and a continuous wave laser interferometer to detect the ultrasound at the point of interrogation to perform ultrasonic inspection. LBU enables in-process measurement since there is no sensor contact or near-contact with the workpiece. The authors are using laser-generated plate waves to propagate form one plate into the weld nugget as a means of detecting defects.
Laser-based ultrasonic (LBU) measurement shows great promise for on-line monitoring of weld quality in tailor-welded blanks. Tailor-welded blanks are steel blanks made from plates of differing thickness and/or properties butt-welded together; they are used in automobile manufacturing to produce body, frame, and closure panels. LBU uses a pulsed laser to generate the ultrasound and a continuous wave laser interferometer to detect the ultrasound at the point of interrogation to perform ultrasonic inspection. LBU enables in-process measurements since there is no sensor contact or near-contact with the workpiece. The authors are using laser-generated plate waves to propagate from one plate into the weld nugget as a means of detecting defects.
Laser-based ultrasonic (LBU) measurement shows great promise for on-line monitoring of weld quality in tailor-welded blanks. Tailor-welded blanks are steel blanks made from plates of differing thicknesses and/or properties butt- welded together, they are used in automobile manufacturing to produce body, frame, and closure panels. LBU uses a pulsed laser to generate the ultrasound and a continuous wave laser interferometer to detect the ultrasound at the point of interrogation to perform ultrasonic inspection. LBU enables in-process measurements since there is no sensor contact or near-contact with the workpiece.
The Anticipatory System (AS) formalism developed by Robert Rosen provides some insight into the problem of embedding intelligent behavior in machines. AS emulates the anticipatory behavior of biological systems. AS bases its behavior on its expectations about the near future and those expectations are modified as the system gains experience. The expectation is based on an internal model that is drawn from an appeal to physical reality. To be adaptive, the model must be able to update itself. To be practical, the model must run faster than real-time. The need for a physical model and the requirement that the model execute at extreme speeds, has held back the application of AS to practical problems. Two recent advances make it possible to consider the use of AS for practical intelligent sensors. First, advances in transducer technology make it possible to obtain previously unavailable data from which a model can be derived. For example, acoustic emissions (AE) can be fed into a Bayesian system identifier that enables the separation of a weak characterizing signal, such as the signature of pump cavitation precursors, from a strong masking signal, such as a pump vibration feature. The second advance is the development of extremely fast, but inexpensive, digital signal processing hardware on which it is possible to run an adaptive Bayesian-derived model faster than real-time. This paper reports the investigation of an AS using a model of cavitation based on hydrodynamic principles and Bayesian analysis of data from high-performance AE sensors.
The feasibility of acoustic resonance for detection of plastic mines was investigated by researchers at the Oak Ridge National Laboratory's Instrumentation and Controls Division under an internally funded program. The data reported in this paper suggest that acoustic resonance is not a practical method for mine detection. Representative small plastic anti- personnel mines were tested, and were found to not exhibit detectable acoustic resonances. Also, non-metal objects known to have strong acoustic resonances were tested with a variety of excitation techniques, and no practical non-contact method of exciting a consistently detectable resonance in a buried object was discovered. Some of the experimental data developed in this work may be useful to other researchers seeking a method to detect buried plastic mines. A number of excitation methods and their pitfalls are discussed. Excitation methods that were investigated include swept acoustic, chopped acoustic, wavelet acoustic, and mechanical shaking. Under very contrived conditions, a weak response that could be attributed to acoustic resonance was observed, but it does not appear to be practical as a mine detection feature. Transfer properties of soil were investigated. Impulse responses of several representative plastic mines were investigated. Acoustic leakage coupling, and its implications as a disruptive mechanism were investigated.
Nuclear Quadrupole Resonance (NQR) is effective for the detecting and identification of certain types of explosives such as RDX, PETN and TNT. In explosive detection, the NQR response of certain 14N nuclei present in the crystalline material is proved. The 14N nuclei possess a nuclear quadrupole moment which in the presence of an electric field gradient produces an energy level splitting which may be excited by radio-frequency magnetic fields. Pulsing on the sample with a radio signal of the appropriate frequency produces a transient NQR response which may then be detected. Since the resonant frequency is dependent upon both the quadrupole moment of the 14N nucleus and the nature of the local electric field gradients, it is very compound specific. Under DARPA sponsorship, the authors are using multiresolution methods to investigate the enhancement of operation of NQR explosives detectors used for mine detection. For this application, NQR processing time must be reduced to less than one second. False alarm response due to acoustic and piezoelectric ringing must be suppressed. Also, as TNT is the most prevalent explosive found in land mines NWR detection of TNT must be made practical despite unfavorable relaxation times. All three issues require improvement in signal-to-noise ratio, and all would benefit from improved feature extraction. This paper reports some of the insights provided by multiresolution methods that can be used to obtain these improvements. It includes results of multiresolution analysis of experimentally observed NQR signatures for RDX response and various false alarm signatures in the absence of explosive compounds.
Oak Ridge National Laboratory and Quantum Magnetics, Inc. are exploring novel landmine detection technologies. Technologies considered here include bioreporter bacteria, swept acoustic resonance, nuclear quadrupole resonance (NQR), and semiotic data fusion. Bioreporter bacteria look promising for third-world humanitarian applications; they are inexpensive, and deployment does not require high-tech methods. Swept acoustic resonance may be a useful adjunct to magnetometers in humanitarian demining. For military demining, NQR is a promising method for detecting explosive substances; of 50,000 substances that have been tested, one has an NQR signature that can be mistaken for RDX or TNT. For both military and commercial demining, sensor fusion entails two daunting tasks, identifying fusible features in both present-day and emerging technologies, and devising a fusion algorithm that runs in real-time on cheap hardware. Preliminary research in these areas is encouraging. A bioreporter bacterium for TNT detection is under development. Investigation has just started in swept acoustic resonance as an approach to a cheap mine detector for humanitarian use. Real-time wavelet processing appears to be a key to extending NQR bomb detection into mine detection, including TNT-based mines. Recent discoveries in semiotics may be the breakthrough that will lead to a robust fused detection scheme.
Under a program sponsored by the Department of Energy, the Oak Ridge complex is developing a `Portal-of-the-Future', or `smart portal.' This is a security portal for vehicular traffic which is intended to quickly detect explosives, hidden passengers, etc. It uses several technologies, including microwaves, weigh-in-motion, digital image processing, and electroacoustic wavelet-based heartbeat detection. A novel component of particular interest is the Enclosed Space Detection System (ESDS), which detects the presence of persons hiding in a vehicle. The system operates by detecting the presence of a human ballistocardiographic signature. Each time the heart beats, it generates a small but measurable shock wave that propagates through the body. The wave, whose graph is called a ballistocardiogram, is the mechanical analog of the electrocardiograms, which is routinely used for medical diagnosis. The wave is, in turn, coupled to any surface or object with which the body is in contact. If the body is located in an enclosed space, this will result in a measurable deflection of the surface of the enclosure. Independent testing has shown ESDS to be highly reliable. The technologies used in the smart portal operate in real time and allow vehicles to be checked through the portal in much less time than would be required for human inspection. Although not originally developed for commercial transportation, the smart portal has the potential to solve several transportation problems. It could relieve congestion at international highway border crossings by reducing the time required to inspect each vehicle while increasing the level of security. It can reduce highway congestion at the entrance of secure facilities such as prisons. Also, it could provide security at intermodal transfer points, such as airport parking lots and car ferry terminals.
The problem of providing an electronic warning of an impending crash to a precrash restraint system a fraction of a second before physical contact differs from more widely explored problems, such as providing several seconds of crash warning to a driver. One approach to precrash restraint sensing is to apply anticipatory system theory. This consists of nested simplified models of the system to be controlled and of the system's environment. It requires sensory information to describe the `current state' of the system and the environment. The models use the sensor data to make a faster-than-real-time prediction about the near future. Anticipation theory is well founded but rarely used. A major problem is to extract real-time current-state information from inexpensive sensors. Providing current-state information to the nested models is the weakest element of the system. Therefore, sensors and real-time processing of sensor signals command the most attention in an assessment of system feasibility. This paper describes problem definition, potential `showstoppers,' and ways to overcome them. It includes experiments showing that inexpensive radar is a practical sensing element. It considers fast and inexpensive algorithms to extract information from sensor data.
The authors have constructed a wavelet processing board that implements a 14-level wavelet transform. The board uses a high-speed analog-to-digital (A/D) converter, a hardware queue, and five fixed-point digital signal processing (DSP) chips in a parallel pipeline architecture. All five processors are independently programmable. The board is designed as a general purpose engine for instrumentation applications requiring near real-time wavelet processing or multiscale filtering. The present application is the processing engine of a magnetic field monitor that covers 305 Hz through 5 MHz. The monitor is used for the detection of peak values of magnetic fields in nuclear power plants. This paper describes the design, development, simulation, and testing of the system. Specific issues include the conditioning of real-world signals for wavelet processing, practical trade-offs between queue length and filter length, selection of filter coefficients, simulation of a 14-octave filter bank, and limitations imposed by a fixed-point processor. Test results from the completed wavelet board are included.
Traffic management can be thought of as a stochastic queuing process where the serving time at one of its control points is dynamically linked to the global traffic pattern, which is, in turn, dynamically linked to the control point. For this closed-loop system to be effective, the traffic management system must sense and interpret a large spatial projection of data originating from multiple sensor suites. This concept is the basis for the development of a traffic flow wide-area surveillance (TFWAS) system. This paper presents the results of a study by Oak Ridge National Laboratory to define the operational specifications and characteristics, to determine the constraints, and to examine the state of technology of a TFWAS system in terms of traffic management and control. In doing so, the functions and attributes of a TFWAS system are mapped into an operational structure consistent with the Intelligent Vehicle Highway System (IVHS) concept and the existing highway infrastructure. This mapping includes identifying candidate sensor suites and establishing criteria, requirements, and performance measures by which these systems can be graded in their ability and practicality to meet the operational requirements of a TFWAS system. In light of this, issues such as system integration, applicable technologies, impact on traffic management and control, and public acceptance are addressed.
We describe a wavelet-based technique for identifying aircraft from acoustic emissions during takeoff and landing. Tests show that the sensor can be a single, inexpensive hearing-aid microphone placed close to the ground. The paper describes data collection, analysis by various techniques, methods of event classification, and extraction of certain physical parameters from wavelet subspace projections. The primary goal of this paper is to show that wavelet analysis can be used as a divide-and- conquer first step in signal processing, providing simplification and noise filtering. The idea is to project the original signal onto the orthogonal wavelet subspaces, both details and approximations. Subsequent analysis, such as system identification, nonlinear systems analysis, and feature extraction, is then carried out on the various signal subspaces.
Optical time-domain reflectometry (OTDR) is a simple and rugged technique for measuring quantities such as strain that affect the propagation of light in an optical fiber. For engineering applications of OTDR, it is important to know the repeatable limits of its performance. The authors constructed an OTDR-based, submillimeter resolution, strain measurement system from off-the-shelf components. The system repeatably resolves changes in time of flight to within +/- 2 ps. Using a 1 m, single-mode fiber as a gauge and observing the time of flight between Fresnel reflections, we observed a repeatable sensitivity of 400 microstrains. Using the same fiber to connect the legs of a 3 dB directional coupler to form a loop, we observed a repeatable sensitivity of 200 microstrains. Realizable changes to the system that should improve the repeatable sensitivity to 20 microstrains or less are discussed.
The measurement of composition of chemical process streams at multiple points is a critical problem in many
industrial environments. We are developing an instrument, based on laboratory Raman spectroscopy, to measure
composition of multiple components in a distillation column. A Nd:YAG laser is used to excite the sample and an
interferometer is used to detect the Raman spectrum. The light from the laser is routed to the measurement points
through a fiber optic probe which also gathers the light generated in the column by the Raman effect. A multiplexer
is introduced to allow sharing of the expensive components of the system among several measurement points. The instrunient
will initially be applied to distillation columns, but should be applicable to analysis of many liquid mixtures
of commercial interest. The response time is approximately three minutes per measurement. The composition
measurement range is from approximately 5% to 100% with an average error of less than 2% RMS. Progress in the
implementation of the instrument will be discussed, with particular emphasis on losses due to optical fiber lengths up
to 35 meters.