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

The RAPid Telescopes for Optical Response (RAPTOR) observatory network consists of several robotic astronomical telescopes primarily designed to search for astrophysical transients called a gamma-ray bursts (GRBs). Although intrinsically bright, GRBs are difficult to detect because of their short duration. Typically, they are first observed by satellites that then relay the coordinates of the GRB to a ground station which, in turn, distributes the coordinates over the internet so that ground based observers can perform follow-up observations. Typically the ground based observations begin after the GRB has ended and only residual emiSSion (the 'afterglow') is left. However, if the satellite relays the GRB coordinates quickly enough, a 'fast' robotic telescope on the ground may be able to catch the GRB in progress. The RAPTOR telescope system is one of only a few in the world to have accomplished this feat. In order to achieve these results, the RAPTOR telescopes must operate autonomously at a high duty-cycle and in peak operating condition. Currently the telescopes are maintained in an ad hoc manner, often in a run-to-failure mode. The RAPTOR project could benefit greatly from a structural health monitoring (SHM) system, especially as more complex units are added to the suite ofmore » telescopes. This paper will summarize preliminary results from an SHM study performed on one of the RAPTOR telescopes. Damage scenarios that are of concern and that have been previously observed are first summarized. Then a specific study of damage to the telescope drive mechanism is presented where the data acquisition system is first described. Next, damage detection algorithms are developed with LANL's new publically available software SHMTools and the results of this process are discussed in detail. The paper will conclude with a summary of future planned refinemenls of the RAPTOR SHM system.« less

Real-time condition assessment of RAPTOR telescope systems

The blossoming of sensing solutions based on the use of carbon materials and the pervasive exploration of compressed sensing (CS) for developing structural health monitoring applications suggest the possibility of combining these two research areas in a novel family of smart structures. Specifically, the authors propose an architecture for security-related applications that leverages the tunable electrical properties of a graphite oxide (GO) paper-based tamper-evident seal with a compressed-sensing (CS) encryption/authentication protocol. The electrical properties of GO are sensitive to the traditional methods that are commonly used to remove and replace paper-based tamper-evident seals (mechanical lifting, solvents, heat/cold temperature changes, steam). The sensitivity of the electro-chemical properties of GO to such malicious insults is exploited in this architecture. This is accomplished by using GO paper to physically realize the measurement matrix required to implement a compressive sampling procedure. The proposed architecture allows the seal to characterize its integrity, while simultaneously providing an encrypted/authentication feature making the seal difficult to counterfeit, spoof, or remove/replace. Traditional digital encryption/authentication techniques are often bit sensitive making them difficult to implement as part of a measurement process. CS is not bit sensitive and can tolerate deviation caused by noise and allows the seal to be robust with respect to environmental changes that can affect the electrical properties of the GO paper during normal operation. Further, the reduced amount of samples that need to be stored and transmitted makes the proposed solution highly attractive for power constrained applications where the seal is interrogated by a remote reader.

A remote-readable graphite oxide (GO) based tamper-evident seal with self-reporting and self-authentication capabilities

This paper discusses how techniques coming from Computational Intelligence (CI) have contributed substantially to the understanding and solution of problems in structural health monitoring (SHM) The paper begins by giving some background to SHM into a number of eras, with the most recent being the Computational Intelligence Era The paper then outlines three case studies regarding novelty detection for three different systems and illustrates alternative machine learning and statistics tools in terms of progressive complexity

Aspects of computational intelligence in structural dynamics: Structural health monitoring

https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-22-25886

Charles R. Farrar

Papers

Real-time condition assessment of RAPTOR telescope systems

A remote-readable graphite oxide (GO) based tamper-evident seal with self-reporting and self-authentication capabilities

Aspects of computational intelligence in structural dynamics: Structural health monitoring

The Los Alamos Dynamic Summer School: An Educational Program Promoting SHM & NDE Research

Health monitoring of civil and aerospace structural components by guided ultrasonic waves