Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach

A failure detection and identification (FDI) scheme is developed for a small remotely controlled jet aircraft based on the Artificial Immune System (AIS) paradigm. Pilot-in-the-loop flight data are used to develop and test a scheme capable of identifying known and unknown aircraft actuator and sensor failures. Negative selection is used as the main mechanism for self/non-self definition; however, an alternative approach using positive selection to enhance performance is also presented. Tested failures include aileron and stabilator locked at trim and angular rate sensor bias. Hyper-spheres are chosen to represent detectors. Different definitions of distance for the matching rules are applied and their effect on the behavior of hyper-bodies is discussed. All the steps involved in the creation of the scheme are presented including design selections embedded in the different algorithms applied to generate the detectors set. The evaluation of the scheme is performed in terms of detection rate, false alarms, and detection time for normal conditions and upset conditions. The proposed detection scheme achieves good detection performance for all flight conditions considered. This approach proves promising potential to cope with the multidimensional characteristics of integrated/comprehensive detection for aircraft sub-system failures.;A preliminary performance comparison between an AIS based FDI scheme and a Neural Network and Floating Threshold based one is presented including groundwork on assessing possible improvements on pilot situational awareness aided by FDI schemes. Initial results favor the AIS approach to FDI due to its rather undemanding adaptation capabilities to new environments. The presence of the FDI scheme suggests benefits for the interaction between the pilot and the upset conditions by improving the accuracy of the identification of each particular failure and decreasing the detection delays

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope.;Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope.;This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems.;Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study.;The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework

Fault detection algorithm for telephone systems using the danger theory

Orientador: Fernando Jose Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Essa dissertação apresenta um algoritmo de detecção de falhas composto de múltiplos módulos interconectados e operando de acordo com o paradigma suportado pela Teoria do Perigo em imunologia. Esse algoritmo busca atingir características significativas que um sistema de detecção de falhas deve expressar ao monitorar um sistema telefônico. Essas características seriam basicamente a adaptabilidade, devido à forte variação que esse sistema pode ter em seus parâmetros ao longo do tempo, e a diminuição no número de falsos positivos que podem ser gerados ao se classificar como falha toda anormalidade encontrada. Cenários simulados foram concebidos para validar a proposta, sendo que os resultados obtidos foram analisados e comparados com propostas alternativasAbstract: Abstract This thesis presents a fault detection algorithm composed of multiple interconnected modules, and operating according to the paradigm supported by the Danger Theory in immunology. This algorithm attempts to achieve significant features that a fault detection system is supposed to express when monitoring a telephone system. These features would basically be adaptability, due to the strong variation that operational conditions may exhibit over time, and the decrease in the number of false positives, which can be generated when any abnormal behavior is erroneously classified as being a fault. Simulated scenarios have been conceived to validate the proposal, and the obtained results are then analyzed and compared with alternative proposalsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Fault Detection Algorithm For Telephone Systems Based On The Danger Theory

This work is aimed at presenting a fault detection algorithm composed of multiple interconnected modules, and operating according to the paradigm supported by the danger theory in immunology. This algorithm attempts to achieve significant features that a fault detection system is supposed to have when monitoring a telephone profile system. These features would basically be adaptability due to the strong variation that operational conditions may exhibit over time, and the decrease in the number of false positives, which can be generated when any abnormal behavior is erroneously classified as being a fault. Simulated scenarios have been conceived to validate the proposal, and the obtained results are then analyzed. © Springer-Verlag Berlin Heidelberg 2005.3627418431Aickelin, U., Bentley, P., Cayzer, S., Kim, J., McLeod, J., Danger theory: The link between AIS and IDS? (2003) 2nd International Conference on AIS (ICARIS 2003), pp. 147-155Atamas, S.P., Les affinities electives (2005) Dossier Pour la Science, 46Ayara, M., Timmis, J., De Lemos, R., De Castro, L.N., Duncan, R., Negative selection: How to generate detectors (2002) 1st International Conference on AIS (ICARIS 2002), pp. 89-98Bersini, H., Self-assertion versus self-recognition: A tribute to Francisco Varela (2002) 1st International Conference on AIS (ICARIS 2002), pp. 107-112De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Faynberg, I., Lawrence, G., Lu, H.-L., (2000) Converged Networks and Services: Internetworking IP and the PSTN, , New York: John Wiley & SonsGonzález, F.A., Dasgupta, D., An immunogenetic technique to detect anomalies in network traffic (2002) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1081-1088Hersent, O., Petit, J.P., (1999) IP Telephony: Packet-based Multimedia Communications Systems, , Addison-WesleyMatzinger, P., Tolerance danger and the extended family (1994) Annual Review of Immunology, 12, pp. 991-1045Sarafijanovic, S., Boudec, J., An artificial immune system for misbehavior detection in mobile ad-hoc networks with virtual thymus, clustering, danger signal, and memory detectors (2004) 3rd International Conference on AIS (ICARIS 2004), pp. 316-329Seeker, A., Freitas, A.A., Timmis, J., A danger theory inspired approach to web mining (2003) 2nd International Conference on AIS, pp. 156-167Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S., Boosting the margin: A new explanation for the effectiveness of voting methods (1998) The Annals of Statistics, 26 (5), pp. 1651-1686Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N., A review of process fault detection and diagnosis: Part I - Quantitative model-based methods (2003) Computer and Chemical Engineering, 27, pp. 293-31