Novelty detection across a small population of real structures: A negative selection approach

Vibration-based Structural Health Monitoring (SHM), exploits a variety of approaches for novelty detection. In particular, many data-based methods try to recognise patterns by exploiting analogies with the human body's natural defences at a cellular level. These algorithms fall within the Artificial Immune System (AIS) class and can be chosen, according to their peculiarities, to solve specific problems in diverse application areas. This study investigates the damage-detection process in different operational conditions, obtained by applying structural modifications to a laboratory-scale aeroplane, which follows the geometric features of the GARTEUR benchmark project. Damage identification is performed by exploiting the Negative Selection Algorithm (NSA), already applied by some of the authors on numerically-simulated case studies, and chosen for its capability of self/non-self discrimination under varying operational or environmental conditions. The research is expanded by using sparse autoencoders for feature dimensionality reduction. The method is applied to an experimental dataset acquired via Scanning Laser Doppler Vibrometer (SLDV) measurements, to identify consistent damage-sensitive features from the frequency response functions, and to obtain a reliable fault-detection performance

Detector Design Considerations in High-Dimensional Artificial Immune Systems

This research lays the groundwork for a network intrusion detection system that can operate with only knowledge of normal network traffic, using a process known as anomaly detection. Real-valued negative selection (RNS) is a specific anomaly detection algorithm that can be used to perform two-class classification when only one class is available for training. Researchers have shown fundamental problems with the most common detector shape, hyperspheres, in high-dimensional space. The research contained herein shows that the second most common detector type, hypercubes, can also cause problems due to biasing certain features in high dimensions. To address these problems, a new detector shape, the hypersteinmetz solid, is proposed, the goal of which is to provide a tradeoff between the problems plaguing hyperspheres and hypercubes. In order to investigate the potential benefits of the hypersteinmetz solid, an effective RNS detector size range is determined. Then, the relationship between content coverage of a dataset and classification accuracy is investigated. Subsequently, this research shows the tradeoffs that take place in high-dimensional data when hypersteinmetzes are chosen over hyperspheres or hypercubes. The experimental results show that detector shape is the dominant factor toward classification accuracy in high-dimensional RNS

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

The design of an evolutionary algorithm for artificial immune system based failure detector generation and optimization

The development of an evolutionary algorithm and accompanying software for the generation and optimization of artificial immune system-based failure detectors is presented in this thesis. These detectors use the Artificial Immune System-based negative selection strategy. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft sub-system abnormal conditions. The evolutionary algorithm and accompanying software discussed in this document is concerned with the creation, optimization, and testing of failure detectors based on the negative selection strategy. A preliminary phase consists of processing data from flight tests for self definition including normalization, duplicate removal, and clustering. A first phase of the evolutionary algorithm produces, through an iterative process, a set of detectors that do not overlap with the self and achieve a prescribed level of coverage of the non-self. A second phase consists of a classic evolutionary algorithm that attempts to optimize the number of detectors, overlapping between detectors, and coverage of the non-self while maintaining no overlapping with the self. For this second phase, the initial population is composed of sets of detectors, called individuals, obtained in the first phase. Specific genetic operators have been defined to accommodate different detector shapes, such as hyper-rectangles, hyper-spheres, hyper-ellipsoids and hyper-rotational-ellipsoids. The output of this evolutionary algorithm consists of an optimized set of detectors which is intended for later use as a part of a detection, identification, and evaluation scheme for aircraft sub-system failure.;An interactive design environment has been developed in MATLAB that relies on an advanced user-friendly graphical interface and on a substantial library of alternative algorithms to allow maximum flexibility and effectiveness in the design of detector sets for artificial immune system-based abnormal condition detection. This user interface is designed for use with Windows and MATLAB 7.6.0, although measures have been taken to maintain compatibility with MATLAB version 7.0.4 and higher, with limited interface compatibility. This interface may also be used with UNIX versions of MATLAB, version 7.0.4 or higher.;The results obtained show the feasibility of optimizing the various shapes in 2, 3, and 6 dimensions. Hyper-spheres are generally faster than the other three shapes, though they do not necessarily exhibit the best detection results. Hyper-ellipsoids and hyper-rotational-ellipsoids generally show somewhat better detection performance than hyper-spheres, but at a higher calculation cost. Calculation time for optimization of hyper-rectangles seems to be highly susceptible to dimensionality, taking increasingly long in higher dimensions. In addition, hyper-rectangles tend to need a higher number of detectors to achieve adequate coverage of the solution space, though they exhibit very little overlapping among detectors. However, hyper-rectangles are consistently and considerably quicker to calculate detection for than the other shapes, which may make them a promising candidate for online detection schemes

Effective Fault Diagnosis in Chemical Plants By Integrating Multiple Methodologies

Ph.DDOCTOR OF PHILOSOPH

Estimating the detector coverage in a negative selection algorithm

This paper proposes a statistical mechanism to analyze the detector coverage in a negative selection algorithm, namely a quantitative measurement of a detector set’s capability to detect nonself data. This novel method has the advantage of statistical confidence in the estimation of the actual coverage. Furthermore, unlike the existing analysis works of negative selection, it doesn’t depend on specific detector representation and generation algorithm. Not only can it be implemented as a procedure independent from the steps to generate detectors, the experiments in this paper showed that it can also be tightly integrated into the detector generation algorithm to control the number of detectors

Estimating the detector coverage in a negative selection algorithm

This paper proposes a statistical mechanism to analyze the detector coverage in a negative selection algorithm, namely a quantitative measurement of a detector set\u27s capability to detect nonself data. This novel method has the advantage of statistical confidence in the estimation of the actual coverage. Furthermore, unlike the existing analysis works of negative selection, it doesn\u27t depend on specific detector representation and generation algorithm, Not only can it be implemented as a procedure independent from the steps to generate detectors, the experiments in this paper showed that it can also be tightly integrated into the detector generation algorithm to control the number of detectors. Copyright 2005 ACM