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

The RAPid Telescopes for Optical Response (RAPTOR) observatory network consists of several ground-based, autonomous, robotic, astronomical observatories primarily designed to search for astrophysical transients called gamma-ray bursts. To make these observations, however, the RAPTOR telescopes must remain in peak operating condition at a high duty-cycle. Currently, the telescopes are maintained in an ad hoc manner, often in a run-to-failure mode. The required maintenance logistics are further complicated by the fact that many of the observatories are situated in remote locations. To ameliorate this situation, an effort has been initiated to develop a structural health monitoring (SHM) system capable of real-time, remote assessment of the RAPTOR telescopes. This paper summarizes the results from that effort. Common damage scenarios are identified to guide the instrumentation of the telescope system. A comprehensive analysis of the data acquired during experimental testing is then presented, highlig...

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Real-Time Condition Assessment of RAPTOR Telescope Systems

Damage detection and prediction for composite plates

Delamination detection in composite laminates using high-frequency P- and S-waves – Part II: Experimental validation

http://www.lukebornn.com/papers/bornn_mssp_2016.pdf

Modeling and diagnosis of structural systems through sparse dynamic graphical models

In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established Damage sensitive features that explicitly consider nonlinear system input/output relationships are extracted from the ARX model Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated with an impedance-based method that uses piezoelectric (PZT) material as both actuators and sensors The analyzed data is obtained from a laboratory experiment of a three-story building modelCopyright © 2003 by ASME

Charles R. Farrar

Papers

Real-Time Condition Assessment of RAPTOR Telescope Systems

Damage detection and prediction for composite plates

Delamination detection in composite laminates using high-frequency P- and S-waves – Part II: Experimental validation

Modeling and diagnosis of structural systems through sparse dynamic graphical models

Application of Frequency Domain ARX Models and Extreme Value Statistics to Impedance-Based Damage Detection