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

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

Developing an integrated software solution for active-sensing SHM

The field of structural health monitoring (SHM) applies damage detection techniques to provide timely in-situ system condition assessment. Previously, researchers have suggested a fundamental axiom for SHM that states, “damage will increase the complexity of a system.” One way this increased complexity can manifest itself is in the increased complexity of sensor data recorded from the structure when damage occurs. The question then becomes how to best quantify the increase in complexity of those data. Information complexity is one such approach and within this framework various information entropy quantities have been proposed as measures of complexity. The literature has shown that there are multiple information entropy measures, including; Shannon Entropy, Renyi Entropy, Permutation Entropy, Sample Entropy, Approximate Entropy, and Spectral Entropy. With multiple measures proposed to quantify information entropy; a study to compare the relative effectiveness of these entropy measures in the context of SHM is needed. Therefore, the objective of this paper is to compare the effectiveness of entropy-based methods in distinguishing between “Healthy” and “Unhealthy” labeled datasets. The labeled datasets considered in this study were obtained from a 4DOF impact oscillator, a rotating machine with a damaged bearing, and an impact oscillator excited by a rotating machine. Furthermore, two methods were used in this study to classify the results from the different entropy measures; Naive-Bayes classification, and K-means clustering. Effectiveness of a given entropy measure is determined by the number of misclassifications produced when compared to the true labels. The analysis showed that entropy measures obtained from data corresponding to sensors closer to the damage source had fewer misclassifications for the datasets tested. For the datasets considered in this study, the researchers concluded that each dataset had a different most effective entropy measure. The study would need to be expanded to include other classification methods and other datasets to define more precisely which entropy measure is the most effective in identifying the increase in complexity associated with damage and, hence, distinguishing between healthy and damaged data.

Comparison of Complexity Measures for Structural Health Monitoring

This report will focus on the estimation of unmeasured dynamic inputs to a structure given a numerical model of the structure and measured response acquired at discrete locations While the estimation of inputs has not received as much attention historically as state estimation, there are many applications where an improved understanding of the immeasurable input to a structure is vital (eg validating temporally varying and spatiallyvarying load models for large structures such as buildings and ships) In this paper, the introduction contains a brief summary of previous input estimation studies Next, an adjoint-based optimization method is used to estimate dynamic inputs to two experimental structures The technique is evaluated in simulation and with experimental data both on a cantilever beam and on a three-story frame structure The performance and limitations of the adjoint-based input estimation technique are discussed

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Input Estimation from Measured Structural Response

This paper presents a novel approach for Lamb wave based structural health monitoring(SHM) in honeycomb aluminum panels. In this study, a suite of three signal processing algorithms are employed to improve the damage detection capability. The signal processing algorithms used include wavelet attenuation, correlation coefficients of power density spectra, and triangulation of reflected waves. Piezoelectric transducers are utilized as both sensors and actuators for Lamb wave propagation. These SHM algorithms are built into a MatLab interface that integrates and automates the hardware and software operations and displays the results for each algorithm to the analyst for side by side comparison. The effectiveness of each of these signal processing algorithms for SHM in honeycomb aluminum panels under a variety of damage conditions is then demonstrated.

Charles R. Farrar

Papers

Developing an integrated software solution for active-sensing SHM

Comparison of Complexity Measures for Structural Health Monitoring

Input Estimation from Measured Structural Response

Active-Sensing Lamb Wave Propagations for Damage Identification in Honeycomb Aluminum Panels

An introduction to the los alamos damage prognosis project