Artificial-Immune-System-Based Detection Scheme for Aircraft Engine Failures

Perhinschi, Mario G.; Porter, Jaclyn; Moncayo, Hever; Davis, Jennifer; Wayne, W. Scott

doi:10.2514/1.52746

Cited by 18 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection performance for the cases of throttle failure and engine reduced efficiency can be improved by considering additional features (such as temperature and pressure) that capture the dynamic fingerprint of these failures, as investigated in Perhinschi, Porter, Moncayo, Davis, and Wayne (2011).…”

Section: Resultsmentioning

confidence: 99%

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Azzawi

Perhinschi

Moncayo

et al. 2015

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Azzawi

Perhinschi

Moncayo

et al. 2015

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

“…For all shapes (except hyperspheres) variable orientation can be considered as determined by an additional N-dimensional vector. For example, for the hyperspherical representation with Nc clusters c i , the self and the self clusters can be expressed as S f c 1 c 2 : : : c Nc g; c i C i Rc i φ 1i φ 2i : : : φ Ni Rc i (27) where C i is the center, and Rc i is the radius of the cluster. For the same hyperspherical representation with Nd detectors d j , the nonself and the detectors can be expressed aŝ S f d 1 d 2 : : : d Nd g; d j D j Rd j φ 1j φ 2j : : : φ Nj Rd j (28) where D j is the center, and Rd i is the radius of the detector.…”

Section: Acm System Design and Implementationmentioning

confidence: 99%

“…Let us assume that the self S and nonselfŜ are defined as sets of Ndimensional hyperspheres according to Eqs. (27) and (28). A directly involved variable (DIV) in the AC is a variable whose alteration or abnormal variation is directly and significantly the result of the AC.…”

Section: Indirect Quantitative Evaluationmentioning

confidence: 99%

“…The immunitybased fault detection [22,23] operates in a similar manner as does the biological immune system, according to the principle of self/nonself discrimination, when it detects exogenous antigens while not reacting to the self cells. An integrated set of methodologies [24][25][26][27] for AIS-based detection and identification of a wide variety of aircraft subsystem failures/damages has been designed and implemented at West Virginia University (WVU). Integrated high-performance AISbased failure detection and identification schemes have been demonstrated to be capable of handling several categories of subsystem abnormal conditions over extended areas of the flight envelope [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

Perhinschi

Moncayo

Azzawi

2014

Journal of Aircraft

Self Cite

View full text Add to dashboard Cite

This paper presents the development of a biologically inspired generalized conceptual framework for the detection, identification, evaluation, and accommodation of aircraft subsystem abnormal conditions. The artificial immune system paradigm in conjunction with other artificial intelligence techniques, analytical tools, and heuristics are used in an attempt to provide a comprehensive solution to the problem of safely operating aircraft under abnormal flight conditions. The main concepts and foundations are established, and methodologies and algorithms for implementation are outlined. The approach addresses directly the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and is expected to facilitate the design of onboard augmentation systems to increase aircraft survivability, improve operation safety, and optimize performance at both normal and abnormal/upset conditions. Results obtained with an example implementation are presented to illustrate the potential of the proposed framework for producing high-performance schemes for aircraft subsystem abnormal condition detection, identification, evaluation, and accommodation.

show abstract

“…Tuning the various parameters of the artificial DC mechanism (such as the number of DCs in the pool and the triggered and nontriggered weights) has to be further investigated in a future research effort. The detection performance for the cases of throttle failure and engine reduced efficiency can be improved by considering additional features (such as temperature and pressure) that capture the dynamic fingerprint of these failures, as investigated in [108].…”

Section: Original DC Algorithmmentioning

confidence: 99%

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

Azzawi¹

View full text Add to dashboard Cite

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. . . DEDICATION . DEDICATION To my parents, Zeki and Anwar, my wife, Rawaa, and my children, Ahmed and Yisra. iii . . ACKNOWLEDGMENTS . ACKNOWLEDGMENTSIt is a great pleasure to take this opportunity to thank my advisor, Dr. Mario G. Perhinschi. Without his true mentoring, indefinite support, and tireless patience, this work would not have been possible. I would also like to express my sincere thanks to the me...

show abstract

Artificial-Immune-System-Based Detection Scheme for Aircraft Engine Failures

Cited by 18 publications

References 27 publications

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

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

Contact Info

Product

Resources

About