Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

物件導向軟體之架構(Object-Oriented Software Construction)探討

A review on machinery diagnostics and prognostics implementing condition-based maintenance

This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.

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Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

This report summarizes the information obtained from static and dynamic tests of scale-model Seismic Category 1 structures (exclusive of containment) on the damping of low-aspect-ratio, reinforced concrete shear walls. The report reviews experimental assessments of damping in low-aspect-ratio shear walls that have been reported in the literature and presents a summary of the types of structures and structural elements tested. It discusses the testing methods and the methods used to determine equivalent viscous damping ratios (both directly and indirectly), a numerical study that examines the accuracy of various methods for estimating damping from measured acceleration input and response data, and tabulates the damping results. The report concludes by graphically showing the changes in the damping of the shear walls as a function of the peak nominal base shear stress experienced by the structure during simulated seismic events. Also included are comparisons of the damping results obtained in this program with those obtained by other investigators.

Damping in low‐aspect‐ratio, reinforced concrete shear walls

The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 unique measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulatedmore » by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to reduce the number of required measurement locations, reduce the time to make measurements, reduce the memory required to store the measurements, and possibly reduce the computational burden to classify the measurements. This work considers the appropriate selection of the signal dictionary used for signal reconstruction, and performs an evaluation of compressed sensing technique's ability to reconstruct ultrasonic images using fewer measurements than would be needed using traditional Nyquist-limited data collection techniques.« less

Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data

This paper extends the work summarized in the accompanying paper {open_quotes}Comparison of Damage Identification Algorithms on Experimental Modal Data From A Bridge.{close_quotes} A finite element model of the continuous three-span portion of the I-40 bridges, which once crossed the Rio Grande in Albuquerque, NM, was constructed. Following the experimental modal analysis, the bridge tests are repeated analytically using benchmarked finite element models of the bridge. A combination of shell and beam elements form the bridge model. Damage was simulated by creating adjacent nodes and disconnecting the elements on either side of the crack. However, because of the discretization of the finite element models only the final three levels of damage were evaluated. In addition to the analytical simulation of the experiments, the girder crack was repositioned at other potential damage locations. Analytical modal parameters were extracted through signal processing techniques, similar to those used in the experimental investigation and subsequently fed into damage identification routines. These routines have been adapted from those presented at past IMAC conferences and are identical to the ones used in the experimental investigation reported in the accompanying paper. This study provides a direct comparison of the relative accuracy of these different damage identification methods when they are applied to a set of standard numerical problems. The numerical models allow a variety of damage scenarios to be studied once the models have been benchmarked against experimental data.

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Damage identification algorithms applied to numerical modal data from a bridge

At Los Alamos National Laboratory (LANL), various algorithms for structural health monitoring problems have been explored in the last 5 to 6 years. The original DIAMOND (Damage Identification And MOdal aNalysis of Data) software was developed as a package of modal analysis tools with some frequency domain damage identification algorithms included. Since the conception of DIAMOND, the Structural Health Monitoring (SHM) paradigm at LANL has been cast in the framework of statistical pattern recognition, promoting data driven damage detection approaches. To reflect this shift and to allow user-friendly analyses of data, a new piece of software, DIAMOND II is under development. The Graphical User Interface (GUI) of the DIAMOND II software is based on the idea of GLASS (Graphical Linking and Assembly of Syntax Structure) technology, which is currently being implemented at LANL. GLASS is a Java based GUI that allows drag and drop construction of algorithms from various categories of existing functions. In the platform of the underlying GLASS technology, DIAMOND II is simply a module specifically targeting damage identification applications. Users can assemble various routines, building their own algorithms or benchmark testing different damage identification approaches without writing a single line of code.

A software tool for graphically assembling damage identification algorithms

This study focuses on defining and comparing response features that can be used for structural dynamics model validation studies Features extracted from dynamic responses obtained analytically or experimentally, such as basic signal statistics, frequency spectra, and estimated timeseries models, can be used to compare characteristics of structural system dynamics By comparing those response features extracted from experimental data and numerical outputs, validation and uncertainty quantification of numerical model containing uncertain parameters can be realized In this study, the applicability of some response features to model validation is first discussed using measured data from a simple test-bed structure and the associated numerical simulations of these experiments Issues that must be considered were sensitivity, dimensionality, type of response, and presence or absence of measurement noise in the response Furthermore, we illustrate a comparison method of multivariate feature vectors for statistical model validation Results show that the outlier detection technique using the Mahalanobis distance metric can be used as an effective and quantifiable technique for selecting appropriate model parameters However, in this process, one must not only consider the sensitivity of the features being used, but also correlation of the parameters being compared

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Charles R. Farrar

Papers

Damping in low‐aspect‐ratio, reinforced concrete shear walls

Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data

Damage identification algorithms applied to numerical modal data from a bridge

A software tool for graphically assembling damage identification algorithms

Feature extraction for structural dynamics model validation