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

In this paper, a new transmissibility analysis method is proposed for the detection and location of damage via nonlinear features in multidegree-of-freedom (MDOF) structural systems. The method is derived based on the transmissibility of nonlinear output frequency response functions (NOFRFs), a concept recently proposed to extend the traditional transmissibility concept to the nonlinear case. The implementation of the method is only based on measured system output responses and by evaluating and analyzing the transmissibility of these system responses at super-harmonics. This overcomes the problems with available techniques, which assume there is one damaged component with nonlinear features in the system and/or require loading on inspected structural systems is measurable. Both numerical simulation studies and experimental data analysis have been conducted to verify the effectiveness and demonstrate the potential practical applications of the new method.

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A New Transmissibility Analysis Method for Detection and Location of Damage via Nonlinear Features in MDOF Structural Systems

Non-linear feature identifications based on self-sensing impedance measurements for structural health assessment

Los Alamos National Laboratory (LANL) has several ongoing programs to identify damage in structures and mechanical systems from changes in their dynamic characteristics. This paper provides a summary of LANL's involvement with this technology, past experiences in this field including damage detection studies on large civil engineering infrastructure and the directions that research in this area will be taking in the future. The research began by taking a strictly model-based approach to the vibration-based damage detection problem. Recent work has recognized that it is more appropriate to view the damage detection problem as an exercise in statistical pattern recognition. Therefore, a general statistical pattern recognition paradigm will be proposed. (4 pages)

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Structural health monitoring at los alamos national laboratory

In the most general terms damage can be defined as changes introduced into a system that adversely effect the current or future performance of that system. Implicit in this definition is the concept that damage is not meaningful without a comparison between two different states of the system, one of which is assumed to represent the initial, and often undamaged, state. This discussion is focused on the study of damage identification in structural and mechanical systems. Therefore, the definition of damage will be limited to changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely effect the current or future performance of that system.

Damage Detection and Evaluation II

This paper presents the development and applications of a miniaturized impedance sensor node for structural health monitoring. The principle behind the impedance-based structural health monitoring technique is to apply high frequency structural excitations (typically higher than 30 kHz) through the surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the piezoelectric transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. Although many proof-of-concept experiments have been performed using the impedance methods, the impedance-measuring device is bulky and impractical for field-use. Therefore, a recently developed, miniaturized, low-cost impedance measurement chip was used to measure and record the electric impedance of a piezoelectric transducer. The performance of this miniaturized and portable device has been compared to our previous results and its effectiveness has been demonstrated in detecting bolt preload changes in a bolted frame structure. Furthermore, the possibility of wireless communication and local signal processing at the sensor node has been investigated by integrating the device with a microprocessor and telemetry.

Charles R. Farrar

Papers

A New Transmissibility Analysis Method for Detection and Location of Damage via Nonlinear Features in MDOF Structural Systems

Non-linear feature identifications based on self-sensing impedance measurements for structural health assessment

Structural health monitoring at los alamos national laboratory

Damage Detection and Evaluation II

A miniaturized electromechanical impedance-based node for the wireless interrogation of structural health