Structured non-self approach for aircraft failure identification within a fault tolerance architecture

Within an immunity-based architecture for aircraft fault detection, identification and evaluation, a structured, non-self approach has been designed and implemented to classify and quantify the type and severity of different aircraft actuators, sensors, structural components and engine failures. The methodology relies on a hierarchical multi-self strategy with heuristic selection of sub-selves and formulation of a mapping logic algorithm, in which specific detectors of specific selves are mapped against failures based on their capability to selectively capture the dynamic fingerprint of abnormal conditions in all their aspects. Immune negative and positive selection mechanisms have been used within the process. Data from a motion-based six-degrees-of-freedom flight simulator were used to evaluate the performance in terms of percentage identification rates for a set of 2D non-self projections under several upset conditions

Evaluating Aircraft Abnormal Conditions Using an Artificial Dendritic Cell Mechanism

Aircraft abnormal conditions evaluation is a vital prerequisite to a successful post-failure accommodation. Moreover, correct flight envelope estimation and protection requires correct evaluation of both the type and severity of the abnormal condition once the failed subsystem is correctly identified. In a previous study, a biologically-inspired dendritic cell mechanism for aircraft sub-system failure detection and identification was proposed and tested with high performance rates. The mechanism uses a set of artificial dendritic cells to process the multiple outcomes of a hierarchical multi-self detection strategy and produces a single detection outcome. Mature biological dendritic cells that migrate from the tissue to the lymph node carry patterns that are specific to the antigen. Similarly, computational units structured as artificial dendritic cells were used to identify the aircraft failed sub-system

Structured Non-Self Approach for Aircraft Failure Identification within an Immunity-Based Fault Tolerance Architecture

Within an immunity-based architecture for aircraft fault detection, identification and evaluation, a structured, non-self approach has been designed and implemented to classify and quantify the type and severity of different aircraft actuators, sensors, structural components and engine failures. The methodology relies on a hierarchical multi-self strategy with heuristic selection of sub-selves and formulation of a mapping logic algorithm, in which specific detectors of specific selves are mapped against failures based on their capability to selectively capture the dynamic fingerprint of abnormal conditions in all their aspects. Immune negative and positive selection mechanisms have been used within the process. Data from a motion-based six-degrees-of-freedom flight simulator were used to evaluate the performance in terms of percentage identification rates for a set of 2D non-self projections under several upset conditions

Artificial Immune System for Comprehensive and Integrated Aircraft Abnormal Conditions Management

Failures, malfunctions, and damage affecting aircraft subsystems, as well as general environmental and dynamic upset conditions, have been consistently identified as the primary sources or aggravating circumstances of the majority of aviation accidents and incidents [1-3]. It is important to properly address safety under normal and abnormal operational conditions throughout the entire life cycle of aerospace systems, including design, production, maintenance, and operation [4], within an thoroughly conducted aircraft health management process [5-8]. Toward this objective, a new computational paradigm, mimicking the biological immune system, has been extended and implemented for aerospace applications in recent years. The formulation of an immunity-inspired framework for comprehensive and integrated system monitoring and control under normal and abnormal operation, specific methods and algorithms, and example implementations are presented in each chapter