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

Structural health monitoring is described in the context of a statistical process control paradigm. This paper demonstrates the application of various statistical process control techniques such as the Shewhart, the exponentially weighted moving average, and the cumulative sum control charts to vibration-based damage diagnosis. The control limits are first constructed based on the measurements obtained from the initial intact structure. Then, new data are monitored against the control limits. A statistically significant number of outliers outside the control limits indicate a system transition from a healthy state to a damage state. Environmental and operation conditions, such as temperature change and the magnitude variation of the input forces, are also incorporated into the monitoring process. Blind tests of various damage cases are conducted without prior knowledge of the actual damage scenarios to evaluate the performance of the presented control chart techniques.

Damage diagnosis using statistical process control

: The basic purpose of the paper is simple; having proposed a set of axioms or "basic truths" regarding Structural Health Monitoring (SHM) in a previous paper, the authors would like to extend the set by the proposal for a new axiom. This axiom relates to an observation that the presence of damage in a structure or system usually results in increased complexity of measured responses or features. It is argued that this observation could lead to principled means of selecting effective features for SHM.

Complexity: A New Axiom for Structural Health Monitoring?

In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage 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 is useful in this case 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 using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.

Application of frequency domain ARX models and extreme value statistics to damage detection

In this paper, we present a feasibility study of using wireless energy transmission systems to provide a required power
for structural health monitoring (SHM) sensor nodes. The goal of this study is to develop SHM sensing systems which
can be permanently embedded in the host structure and do not require an on-board power sources. With this approach,
the energy will be periodically delivered as needed to operate the sensor node, as opposed to being harvested as in the
conventional approaches. The wirelessly transmitted microwave energy is captured by a microstrip patch antenna, and
then transformed into DC power by a rectifying circuit and stored in a storage medium to provide the required energy to
the sensor and transmitter. Based on the fact that recent networked sensor systems require power on the order of
fractions of a watt, it is quite possible to operate such sensing devices completely from the captured wirelessly delivered
energy. The method of designing and optimizing a wireless energy transmission system is discussed. This paper also
summarizes considerations needed to design such energy delivery systems, experimental procedures and results, and
additional issues that can be used as guidelines for future investigations.

Wireless energy transmission for structural health monitoring embedded sensor nodes

This paper describes a new software package, SHMTools, for prototyping algorithms for various structural health
monitoring (SHM) applications. The software includes a set of standardized MATLAB routines covering three
main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A
subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled
into generic "C" programs to be run on a target hardware. The software is designed to accommodate multiple
sensing modalities, including piezoelectric active-sensing, which have become widely used in SHM practice. The
software package, standardized datasets, and detailed documentation are publicly available for use by the SHM
community. The details of this software will be discussed, along with several example processes to demonstrate
its utility.

Charles R. Farrar

Papers

Damage diagnosis using statistical process control

Complexity: A New Axiom for Structural Health Monitoring?

Application of frequency domain ARX models and extreme value statistics to damage detection

Wireless energy transmission for structural health monitoring embedded sensor nodes

SHMTools: a new embeddable software package for SHM applications