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

Damage detection using wavelet transforms for theme park rides

Adaptive Positive Position Feedback for Structural Control

Autonomous execution of the Precision Immobilization Technique

In vibration-based structural damage assessment, the proper selection and extraction of appropriate features remains an important component of the overall process. These features ideally must be both sensitive to a particular damage scenario and insensitive to ambient influences within some statistical confidence. A novel approach has been introduced in previous works which described the local attractor variance ratio as a state-space-based feature (in combination with appropriate chaotic excitation input), which was shown in numerical experiments to be superior to modal-based features in both detecting and quantifying damage under several scenarios. In this work, we apply this new technique to a simple five-degree-of-freedom experimental system, where damage is induced through a spring stiffness change. In addition, we present a variation of this feature, both of which seek to quantify states space distortion with damage.

Experimental demonstration of local attractor variance as a damage indication feature

A current technique that has been the subject of a great deal of study in the structural health monitoring community is the impedance method, which uses high frequency responses to monitor a local area of a structure for changes in structural impedance that indicate damage. To date, piezoceramic sensors, whose electrical impedance is directly coupled with the structure’s mechanical impedance, have been used as both sensors and actuators for impedance measurements and this practice is fairly well developed. However because these sensors are ceramic, they are brittle, making them vulnerable to accidental breakage. They also conform poorly to curved surfaces. In this study macro-fiber composite (MFC) patches, which are flexible in nature, are examined for feasibility of use with the impedance method. Two test structures with both MFC patches and PZTs are considered. Both traditional linear features and newer nonlinear features are used for structural health monitoring. High frequency responses of the MFC patches are examined and they yield damage identification results that are comparable to piezoelectric impedance signals and indicate that MFCs are suitable for impedance sensing and other high-frequency structural health monitoring.

Charles R. Farrar

Papers

Damage detection using wavelet transforms for theme park rides

Adaptive Positive Position Feedback for Structural Control

Autonomous execution of the Precision Immobilization Technique

Experimental demonstration of local attractor variance as a damage indication feature

Structural Health Monitoring Using Macro-Fiber Composites and Impedance Methods