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.

/pdf/damage-identification-and-health-monitoring-of-structural-3s8adkph2w.pdf

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

A unique summer educational program focusing on engineering dynamics has been developed and implemented at Los Alamos National Laboratory. The purpose of this summer school is to expose a select group of students to the broad field of engineering dynamics with the hopes that they will be motivated to pursue this area of research in their graduate studies. The summer school activities included 1) lectures on various engineering topics such as computational structural dynamics, experimental modal analysis, random vibrations, signal processing, etc., 2) a distinguished lecturer series in which prominent guest lecturers gave talks about "cutting edge research" in structural dynamics, 3) field trips and 4) an eight week project having both an analytical and an experimental component. This paper presents an overview of the program.

The los alamos dynamics summer school

The authors of this summary would like to thank the following people whose work is summarized in this paper: William Baker, Philip Cornwell, Tim Darling, Thomas Duffey, Norman Hunter, Albert Migliori. The managers, support personnel, university students, and collaborators who have been involved in these studies also deserve more credit than can be offered herein. Los Alamos National Laboratory is operated by the University of California for the United States Department of Energy.

Structural health monitoring activities at los alamos national laboratory

This paper gives a brief overview of a new project at LANL in structural damage identification for wind turbines. This project makes use of modeling capabilities and sensing technology to understand realistic blade loading on large turbine blades, with the goal of developing the technology needed to automatically detect early damage. Several structural health monitoring (SHM) techniques using piezoelectric active materials are being investigated for the development of wireless, low power sensors that interrogate sections of the wind turbine blade using Lamb wave propagation data, frequency response functions (FRFs), and time-series analysis methods. The modeling and sensor research will be compared with extensive experimental testing, including wind tunnel experiments, load and fatigue tests, and ultrasonic scans - on small- to mid-scale turbine blades. Furthermore, this study will investigate the effect of local damage on the global response of the blade by monitoring low-frequency response changes.

Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation

Extreme value statistics from differential evaluation for damage detection

Note: Chapter 5, Structural Identification of Constructed Systems Reference EPFL-CHAPTER-191194 Record created on 2013-12-10, modified on 2016-08-09

Charles R. Farrar

Papers

The los alamos dynamics summer school

Structural health monitoring activities at los alamos national laboratory

Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation

Extreme value statistics from differential evaluation for damage detection

Structural Identification for Selection, Application, and Calibration of Physics-Based Models