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

In this paper, we present experimental investigations using energy harvesting and wireless energy transmission to
operate embedded structural health monitoring sensor nodes. The goal of this study is to develop sensing systems
that can be permanently embedded within a host structure without the need for an on-board power source. With this
approach the required energy will be harvested from the ambient environment, or periodically delivered by a RF
energy source to supplement conventional harvesting approaches. This approach combines several transducer types
to harvest energy from multiple sources, providing a more robust solution that does not rely on a single energy
source. Both piezoelectric and thermoelectric transducers are considered as energy harvesters to extract the ambient
energy commonly available on civil structures such as bridges. Methods of increasing the efficiency, energy storage
medium, target applications and the integrated use of energy harvesting sources with wireless energy transmission
will be discussed.

Energy harvesting and wireless energy transmission for embedded sensor nodes

The paper presents a discussion of the design, development, and assembly of Structural Health Monitoring (SHM)
experiments launched in space on a sub-orbital flight. Onboard experiments were focused on investigating the utility of
piezoelectric wafer active sensors (PWAS) as active elements of spacecraft SHM systems and the electro-mechanical
impedance method as a promising SHM methodology for space systems. A Magneto-elastic active sensor (MEAS) was
used to record in-flight dynamics of the payload. The list of PWAS experiments included a bolted-joint experiment, an
adhesive endurance experiment, and an experiment to monitor PWAS condition during spaceflight. Electromechanical
impedances of piezoelectric sensors were recorded in-flight at varying input frequencies using onboard microcontroller
units. PWAS and MEAS data were recovered from the payload after landing. Details of the sub-orbital flight
experiments are considered and conclusions pertaining to flight results are presented. The paper discusses issues
encountered during design, development, and assembly of the payload and aspects central to successful demonstration of
the SHM during sub-orbital space flight.

Design, development, and assembly of sub-orbital space flight structural health monitoring experiment

Developing damage prognosis solution

Sensing and Data Acquisition Issues for Damage Prognosis

Structural health monitoring (SHM) requires a rigorous approach to designing the sensing system used for the data-acquisition portion of a problem. To date, most SHM sensor system designs are done in a somewhat ad hoc manner where the engineer picks a sensing system that is readily available and that he or she is familiar with, and then attempts to demonstrate that a specific type of damage can be detected with that system. More recently, researchers have begun to develop sensor networks designed specially for SHM. On the basis of this research, several SHM sensor network paradigms have emerged, and this article is intended to provide an overview of these paradigms. First, a brief summary of the statistical pattern-recognition approach to SHM problem is presented. The data-acquisition portion of the paradigm is then addressed in detail, where the various parameters of the system that must be considered in its design and subsequent field deployment are summarized. This article focuses on several general SHM sensor network paradigms that have emerged and concludes by summarizing axioms for SHM that have been proposed in the literature and that are directly related to sensing issues for SHM.


Keywords:

structural health monitoring;
sensor networks;
sensor nodes

Charles R. Farrar

Papers

Energy harvesting and wireless energy transmission for embedded sensor nodes

Design, development, and assembly of sub-orbital space flight structural health monitoring experiment

Developing damage prognosis solution

Sensing and Data Acquisition Issues for Damage Prognosis

Sensor Network Paradigms