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

الفن التشكيلي متضافراً مع الأدب العالمي: شهادة ورؤية عراقية / Plastic Art Intertwined with World Literature: An Iraqi Testimony

[In this testimony, the artist details his fondness for artistic handicrafts since childhood which developed later into a passion for painting, especially after meeting his artistic mentor Hafidh Ad-Douroubi. He reminisces about his years at the Institute of Art in Baghdad and his memories of some of the pioneering artists of the time. He delineates his trajectory of artistic production and participation in various individual and collective exhibits in Iraq and abroad. In his work in painting, etching, and sculpture, he collaborates with authors to produce work that crosses the line between the artistic and the literary and draws upon ancient Iraqi epics and modern Arabic poetry. يتناول الكاتب ولعه بالأعمال اليدوية منذ الصغر، التي تطورت فيما بعد إلى شغف بالرسم عبر تعرفه على الفنان حافظ الدروبي الذي شجعه على الانضمام إلى جماعته الفنية. ويروي كيف تعرّف على أعمال الفنانين الرواد عبر دراسته في معهد الفنون في بغداد. كما يشير إلى مشاركته في معارض جماعية أو شخصية داخل العراق وخارجه. يوضح الكاتب كيف انشغل بالرسم والطباعة وعبرهما تعرف على النحت، حيث تنوعت نتاجاته بين اللوحة وكتاب الفنان أو المجموعات الطباعية المحدودة، وعبر هذه الأعمال، رسم بعض الملاحم العراقية القديمة مثل ملحمة جلجامش إلى جانب قصائد العديد من الشعراء العرب المعاصرين .

Artificial Dendritic Cell Mechanism for Aircraft Immunity-Based Failure Detection and Identification

Biological dendritic cells perform a complex activation/suppression role in the generation, direction, and control of antibodies. Their action relies on balancing information regarding the external antigen type, amount, and virulence, as well as the state and resources of the host organism. In this paper, an information processing algorithm inspired by the functionality of the dendritic cells is proposed to enhance aircraft subsystem abnormal condition detection and identification within the artificial immune system paradigm. A hierarchical multi-self-strategy is used to produce multiple failure detection and identification outcomes at each sample time over a time window. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets this information to decide upon a unique detection and identification outcome with reduced false alarms and a low number of incorrect identifications. A mathematical formulation of the concept and a detailed implementation algorithm are provided. The proposed methodology is demonstrated using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, the detection and identification scheme achieves excellent detection and identification rates with practically no false alarms

Simplified Estimation Algorithms for Aircraft Structural Damage Effects Using an Artificial Immune System

The development of simplified models for the estimation of flight envelope reduction under damages to aircraft structural components, within the artificial immune system paradigm, is presented in this paper. The proposed methodology is part of a comprehensive and integrated framework for aircraft abnormal condition detection, identification, evaluation, and accommodation aimed at ensuring aircraft high survivability rates and operation safety. An artificial immune system built through simulation for a fighter aircraft is used in conjunction with a hierarchical multi-self strategy for estimating ranges of flight envelope relevant variables under structural damages affecting the wing, the horizontal tail, and the vertical tail. Flight envelope ranges are predicted when the lift-generating capabilities of the main aerodynamic surfaces are reduced, based on algorithms that are tailored to the nature and characteristics of the failure and 2-dimensional self projections. The performance of the proposed approach is evaluated using ad-hoc metrics and demonstrated successfully through simulation tests in a motion-based flight simulator

Generation of Artificial Immune System Antibodies Using Raw Data and Cluster Set Union

Within the artificial immune system paradigm for system abnormal condition detection, two approaches can be used for the generation of antibodies/detectors from multiple sets of experimental data defining the normal system operation, or “self”. The raw data set union-based approach consists of collecting all experimental data in one file, which is then used for antibody generation. The cluster set union-based approach consists of clustering each set of experimental data, collecting all the clusters in one set, and then generating the antibodies. In this paper, the two approaches are described, discussed, and their advantages and disadvantages analyzed based on examples obtained during the development of a comprehensive artificial immune system for aircraft sub-system abnormal condition detection. Data from a motion based six degrees-of-freedom flight simulator are used. The detection performance in terms of percentage detection rate and false alarms of the two sets of detectors has been compared for a sub-set of relevant projections under several types of failure and resulted, in general, to be similar. The raw data set union method requires large computer memory, but the total computation time is lower. The cluster set union method can be implemented on lower memory computers; however, the overall computation time increases, unless parallel computation is used

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

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

Evaluation of Aircraft Sensor Failures Effects Using the Artificial Immune System Paradigm

This paper presents the development of simple models for the assessment of flight envelope reduction under aircraft sensor failures within the artificial immune system paradigm. The proposed methodology is expected to facilitate the design of on-board augmentation systems increasing aircraft survivability and improving operation safety. It demonstrates and exploits the capability of the artificial immune system to not only detect and identify aircraft subsystem abnormal conditions but also evaluate their impact and consequences. An artificial immune system built through simulation for a fighter aircraft is used in conjunction with a hierarchical multi-self strategy for estimating ranges of flight envelope relevant variables at post-failure conditions affecting angular rate sensors that are needed within the control augmentation system. Failure-specific algorithms correlated with the characteristics and dimensionality of self projections are developed for roll, pitch, and yaw rate sensors affected by bias. Metrics for the performance evaluation of the proposed approach are defined and used for successful demonstration in a motion-based flight simulator

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

Successful fault-tolerant control strategies often require vital tools that can accurately detect the failure, identify its root cause, and evaluate its nature and severity. Most of the existing methodologies in the field of failure detection, identification, and evaluation are limited to few subsystems with reduced number of features. Due to the complexity and multidimensionality of the aircraft system, new methodologies that are robust, accurate, and fast enough need to be developed for such systems. The biological immune system is a natural system that possesses vigorous peculiarities in protecting the mammalian body from harmful intruders and, therefore, may represent a rich source of inspiration to solve anomaly problems. This paper presents a novel integrated scheme for aircraft sub-system failure detection, identification, and evaluation based on the functionality of the biological dendritic cells and their interactions with the various components of the immune system. The proposed approach relies on using the self/nonself discrimination principle with the hierarchical multiself strategy to overcome the multidimensionality issues. The information collected by the artificial dendritic cells is fused in a way that convert the identification and evaluation problem into a pattern recognition problem. The proposed scheme was successfully tested for a supersonic fighter aircraft in a motion-based flight simulator with high detection, identification, and evaluation rates and practically zero false alarms

Immunity-Based Accommodation of Aircraft Subsystem Failures

Purpose:This paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system failures/damages.Design/methodology/approach:The approach is based on building an artificial memory, which represents self- (nominal conditions) and non-self (abnormal conditions) within the artificial immune system paradigm. Self- and non-self are structured as a set of memory cells consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight. The accommodation algorithm is based on the cell in the memory that is the most similar to the in-coming measurement. Once the best match is found, control commands corresponding to this match are extracted from the memory and used for control purposes.Findings:The results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and capability of the artificial-immune-system-based scheme to accommodate an actuator malfunction, maintain control and complete the task.Research limitations/implications:This paper concentrates on investigation of the possibility of extracting compensatory pilot commands. This is a preliminary step toward a more comprehensive solution to the aircraft abnormal condition accommodation problem.Practical implications:The results demonstrate the effectiveness of the proposed approach using a motion-based flight simulator for actuator and sensor failures.Originality/value:This research effort is focused on investigating the use of the artificial immune system paradigm for control purposes based on a novel methodology