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

Various experimental studies have demonstrated that an impedance-based method is an effective means of structural damage detection. Using the self-sensing and active-sensing capabilities of piezoelectric materials, the electromechanical impedance response can be monitored to provide a qualitative indication of the overall health of a structure. In this article, two new signal processing tools for the impedance method are described in order to improve the damage detection capability and to reduce the amount of data to process for structural health assessment. The first approach is to instantaneously correlate the impedance data between different sensor sets, as opposed to be correlated to pre-stored baseline data. Another approach is to use the pre-defined parameter of impedance data to establish a generalized baseline for bolted joint monitoring. These approaches could reduce the number of data sets and could be efficiently used for low-power impedance devices. The proposed signal processing techniques ar...

Relative baseline features for impedance-based structural health monitoring

Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications

The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex bottleneck structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a grand challenge due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.

http://iopscience.iop.org/article/10.1088/1742-6596/382/1/012047/pdf

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

The aeroelastic characteristics of a cantilevered composite panel of large aspect ratio and with an edge crack are investigated. The panel consists of several fiber-reinforced composite plies, and ...

Crack-induced Changes in Divergence and Flutter of Cantilevered Composite Panels

A simplified approach to the dynamic finite element modeling of composite girder-slab bridges is presented using the limiting case of a single beam element to represent the girder-slab cross section. Dynamic properties calculated with this simplified model are compared with experimental results obtained from an in situ composite girder bridge and with more detailed shell element model predictions. The simplified beam element model accurately calculates the mode shapes of the structure. This agreement, however, is dependent on accurate modeling of the piers and kinematic constraints between the bridge and piers and the appropriate representation of the bridge's torsional properties. The calculated resonant frequencies associated with these modes show some discrepancy when compared with the experimental results. This discrepancy is attributed to the inability of the single beam element model to simulate the three-dimensional boundary conditions found in the actual structure. The simple models provide approximations to the dynamic properties that are accurate enough to be useful in preliminary seismic scoping studies and in the simplified modeling of long, multispan bridges.

Charles R. Farrar

Papers

Relative baseline features for impedance-based structural health monitoring

Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

Crack-induced Changes in Divergence and Flutter of Cantilevered Composite Panels

Bridge modal properties using simplified finite element analysis