Abstract
Purpose
This paper aims to present the development of prediction models for aircraft actuator failure impact on flight envelope within the artificial immune system (AIS) paradigm.
Design/methodology/approach
Simplified algorithms are developed for estimating ranges of flight envelope-relevant variables using an AIS in conjunction with the hierarchical multi-self strategy. The AIS is a new computational paradigm mimicking mechanisms of its biological counterpart for health management of complex systems. The hierarchical multi-self strategy consists of building the AIS as a collection of low-dimensional projections replacing the hyperspace of the self to avoid numerical and conceptual issues related to the high dimensionality of the problem.
Findings
The proposed methodology demonstrates the capability of the AIS to not only detect and identify abnormal conditions (ACs) of the aircraft subsystem but also evaluate their impact and consequences.
Research limitations/implications
The prediction of altered ranges of relevant variables at post-failure conditions requires failure-specific algorithms to correlate with the characteristics and dimensionality of self-projections. Future investigations are expected to expand the types of subsystems that are affected and the nature of the ACs targeted.
Practical implications
It is expected that the proposed methodology will facilitate the design of on-board augmentation systems to increase aircraft survivability and improve operation safety.
Originality/value
The AIS paradigm is extended to AC evaluation as part of an integrated and comprehensive health management process system, also including AC detection, identification and accommodation.