Immunity-Based Aircraft Fault Detection System

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further minimized to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

[1]  Jovan D. Boskovic,et al.  Intelligent Adaptive Control of a Tailless Advanced Fighter Aircraft Under Wing Damage , 2000 .

[2]  William Blake DEVELOPMENT OF THE C-17 FORMATION AIRDROP ELEMENT GEOMETRY , 1997 .

[3]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[4]  Fernando Niño,et al.  A Novel Immune Anomaly Detection Technique Based on Negative Selection , 2003, GECCO.

[5]  Lee,et al.  [American Institute of Aeronautics and Astronautics 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Austin, Texas ()] 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Aeroelastic Studies on a Folding Wing Configuration , 2005 .

[6]  Spyros Xanthakis,et al.  Immune System and Fault-Tolerant Computing , 1995, Artificial Evolution.

[7]  Shi Wengang,et al.  Negative-selection algorithm based approach for fault diagnosis of rotary machinery , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[8]  M. Araujo,et al.  Fault detection system in gas lift well based on artificial immune system , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[9]  Marc L. Steinberg,et al.  Comparison of Intelligent, Adaptive, and Nonlinear Flight Control Laws , 1999 .

[10]  Andrew M. Tyrrell,et al.  Hardware fault tolerance: an immunological solution , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[11]  R. Bodson,et al.  Multivariable adaptive algorithms for reconfigurable flight control , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[12]  Meir Pachter,et al.  Fault tolerant flight control , 2003 .

[13]  Kevin A. Wise,et al.  Flight Testing of Reconfigurable Control Law on the X-36 Tailless Aircraft , 2001 .

[14]  Karen Gundy-Burlet,et al.  An Adaptive Critic Approach to Reference Model Adaptation , 2003 .

[15]  John Kaneshige,et al.  INTEGRATED NEURAL FLIGHT AND PROPULSION CONTROL SYSTEM , 2001 .

[16]  R. O. Canham,et al.  A MULTILAYERED IMMUNE SYSTEM FOR HARDWARE FAULT TOLERANCE WITHIN AN EMBRYONIC ARRAY , 2002 .

[17]  R. Mehra,et al.  Multiple-Model Adaptive Flight Control Scheme for Accommodation of Actuator Failures , 2002 .

[18]  Oscar Castillo,et al.  Intelligent control of aircraft dynamic systems with a new hybrid neuro-fuzzy-fractal approach , 2002, Inf. Sci..

[19]  Y. M. Chen,et al.  Neural networks-based scheme for system failure detection and diagnosis , 2002, Math. Comput. Simul..

[20]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[21]  David W. Corne,et al.  An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems , 2003, ICARIS.

[22]  Rube B. Williams,et al.  Adaptive State Filtering for Space Shuttle Main Engine Turbine Health Monitoring , 2003 .

[23]  Youdan Kim,et al.  Reconfigurable Flight Control System Design Using Direct Adaptive Method , 2003 .

[24]  Fabio A. González,et al.  Anomaly Detection Using Real-Valued Negative Selection , 2003, Genetic Programming and Evolvable Machines.

[25]  Kalmanje KrishnaKumar,et al.  Artificial Immune System Approaches for Aerospace Applications , 2003 .

[26]  Karen Gundy-Burlet,et al.  CONTROL REALLOCATION STRATEGIES FOR DAMAGE ADAPTATION IN TRANSPORT CLASS AIRCRAFT , 2003 .

[27]  Mario G. Perhinschi,et al.  Online Parameter Estimation Techniques Comparison Within a Fault Tolerant Flight Control System , 2002 .

[28]  Anthony J. Calise,et al.  FAULT TOLERANT FLIGHT CONTROL VIA ADAPTIVE NEURAL NETWORK AUGMENTATION , 1998 .