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

A review of shape memory alloy research, applications and opportunities

Numerical Initial Value Problems in Ordinary Differential Equations

Novelty detection: a review—part 1: statistical approaches

With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

Outlier Analysis

Preface. Introduction. Abbreviations. Part I: Background Material. 1. Vibration of Single Degree of Freedom Systems. 2. Vibration of Multiple Degree of Freedom Systems. 3. Vibration of Continuous Systems - Assumed Shapes Approach. 4. Vibration of Continuous Systems - Discretization Approach. 5. Introduction to Steady Aerodynamics. 6. Introduction to Loads. 7. Introduction to Control. Part II: Introduction to Aeroelasticity and Loads. 8. Static Aeroelasticity - Effect of Wing Flexibility on Lift Distribution and Divergence. 9. Static Aeroelasticity - Effect of Wing Flexibility on Control Effectiveness. 10. Introduction to Unsteady Aerodynamics. 11. Dynamic Aeroelasticity - Flutter. 12. Aeroservoelasticity. 13. Equilibrium Manoeuvres. 14. Flight Mechanics Model for Dynamic Manoeuvres. 15. Dynamic Manoeuvres. 16. Gust and turbulence Encounters. 17. Ground Manoeuvres. 18. Aircraft Internal Loads. 19. Potential Flow Aerodynamics. 20. Coupling of Structural and Aerodynamic Computational Models. Part III: Introduction to Industrial Practice. 21. Aircraft Design and Certification. 22. Aeroelasticity and Loads Models. 23. Static Aeroelasticity and Flutter. 24. Flight Manoeuvre and Gust/Turbulence Loads. 25. Ground Manoeuvre Loads. 26. Testing relevant to Aeroelasticity and Loads. Appendices. A. Aircraft Rigid Body Modes. B. Table of Longitudinal Aerodynamic Derivatives. C. Aircraft Symmetric Flexible Modes. D. Model Condensation. E. Aerodynamic Derivatives in body Fixed Axes. F. Aircraft Antisymmetric Flexible Modes. References. Index. Programs Accessible (on the Companion Website) via the Internet. G. MATLAB/SIMULINK Programs for Vibration. H. MATLAB/SIMULINK Programs for Flutter. I. MATLAB/SIMULINK Programs for Flight/Ground Manoeuvres and Gust/Turbulence Encounters.

Introduction to Aircraft Aeroelasticity and Loads

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

a Combined Modal/finite Element Analysis Technique for the Dynamic Response of a Non-Linear Beam to Harmonic Excitation

Blade-tip timing measurement of synchronous vibrations of rotating bladed assemblies

The experimental determination of the vibration characteristics of rotating engine blades is very important for fatigue failure considerations. One of the most promising techniques for measuring the frequency of blade vibrations is blade tip timing. In this paper, three vibration analysis methods were specifically formulated and applied to the tip timing problem for the first time, using data obtained from a simple mathematical blade tip timing simulation. The results from the methods were compared statistically in order to determine which of the techniques is more suitable. One of the methods, the global autoregressive instrumental variables approach, produced satisfactory results at realistic noise levels. However, all of the techniques produced biased results under certain circumstances.

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Jonathan E. Cooper

Papers

Introduction to Aircraft Aeroelasticity and Loads

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

a Combined Modal/finite Element Analysis Technique for the Dynamic Response of a Non-Linear Beam to Harmonic Excitation

Blade-tip timing measurement of synchronous vibrations of rotating bladed assemblies

A Comparison of Blade Tip-Timing Data Analysis Methods