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

An Introduction to Damage Prognosis

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can allow for a rapid assessment of structural integrity after an event such as a natural disaster puts the reliability of civil infrastructure in question. Unfortunately, there are many technical challenges associated with employing such a WSN in civil infrastructure for operation over multiple decades. Maintenance costs must remain low enough to justify the integration of such a WSN into a given structure. The technical challenges include ensuring power is delivered to the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data collected by a wireless sensor network. Here we explore possible solutions to the technical challenges presented by WSN for SHM applications. A “mobile host” WSN has been developed where a civil structure is instrumented with sensor nodes capable of being powered solely on energy transmitted to the sensor node wirelessly by the mobile host. When the sensor node has received adequate energy for making a given measurement, the sensor node performs the necessary measurement operations and then wirelessly transmits the measurement to the mobile host. These operations are then repeated for all desired sensor nodes in the network. In crisis situations such as an earthquake, it is good to have tools in place allowing a rapid condition assessment of civil infrastructures. Often these situations do not allow conventional human inspections due to safety and accessibility issues. It would be desirable to automate the inspection process so that humans are not placed in danger during the assessment and to employ relevant data collection and feature extraction methods to eliminate the need for infrastructure assessment experts at a potentially damaged location. Furthermore, this rapid assessment-monitoring system should be robust enough to be deployed on a structure for multiple decades without any human intervention. The work presented here proposes sensors and nodes that can be integrated into such a rapid assessment-monitoring framework. The sensor nodes investigated here are designed to be placed into a “roving-host,” wireless-sensor network. In this framework an unmanned aerial vehicle (UAV) is used to fly to the sensors of interest. Once the UAV arrives at the relevant sensor node, it wirelessly transmits energy to the sensor node to power it up in a manner similar to the operation of a radio frequency identification (RFID) tag. The sensor node makes a measurement and then transmits data back to the UAV. The UAV then adds the sensor node’s data to its database and repeats the process for other relevant nodes. Such a network allows for periodic energy delivery as well as a centralized data processing capability

Wireless Sensor Technologies for Monitoring Civil Structures

13. Fundamental Axioms of Structural Health Monitoring

In this article, we present experimental investigations using energy harvesting and wireless energy transmission to power wireless 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 radio-frequency 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 ener...

Charles R. Farrar

Papers

An Introduction to Damage Prognosis

Wireless Sensor Technologies for Monitoring Civil Structures

13. Fundamental Axioms of Structural Health Monitoring

Energy Harvesting and Wireless Energy Transmission for Embedded SHM Sensor Nodes

Damage detection in initially nonlinear systems