A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities

Progress in Aerospace Sciences

Volumn 46, Issue 7, 2010, Pages 247-273

  Lopez, Israel ^a   Sarigul Klijn, Nesrin ^a  

^a University of California   (United States)

Author keywords

Control; Diagnostics; Flight vehicles; Structural health monitoring; Uncertainties

Indexed keywords

DAMAGE ASSESSMENTS; DAMAGE SENSING; DAMAGED STRUCTURES; DIAGNOSTICS; EXISTING METHOD; FLIGHT VEHICLES; RESEARCH CHALLENGES; STRUCTURAL DAMAGE MONITORING; STRUCTURAL DAMAGES; STRUCTURAL HEALTH; UNCERTAINTIES;

DAMAGE DETECTION; DIAGNOSIS; STRUCTURAL HEALTH MONITORING; STRUCTURES (BUILT OBJECTS); VEHICLES;

UNCERTAINTY ANALYSIS;

EID: 77955917931     PISSN: 03760421     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.paerosci.2010.03.003     Document Type: Review

References (190)

1. EP Carden, and P. Fanning Vibration based condition monitoring: a review Structural Health Monitoring 3 2004 355 377
2. SD Fassois, and JS. Sakellariou Time-series methods for fault detection and identification in vibrating structures Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 2007 411 448
3. AKS Jardine, D Lin, and D. Banjevic A review on machinery diagnostics and prognostics implementing condition-based maintenance Mechanical Systems and Signal Processing 20 2006 1483 1510
4. SA Neild, PD McFadden, and MS. Williams A review of time-frequency methods for structural vibration analysis Engineering Structures 25 2003 713 728
5. WJ Staszewski, and AN. Robertson Timefrequency and timescale analyses for structural health monitoring Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 2007 449 477
6. MMR Taha, A Noureldin, JL Lucero, and TJ. Baca Wavelet transform for structural health monitoring: a compendium of uses and features Structural Health Monitoring 5 2006 267 295
7. K Worden, and JM. Dulieu-Barton An overview of intelligent fault detection in systems and structures Structural Health Monitoring 3 2004 85 98
8. Worden K, Farrar CR, Haywood J, Todd M. A review of nonlinear dynamics applications to structural health monitoring. Structural Control and Health Monitoring 2007; In Press.
9. K Worden, and G. Manson The application of machine learning to structural health monitoring Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 2007 515 537
10. Heng A, Zhang S, Tan ACC, Mathew J. Rotating machinery prognostics: state of the art, challenges and opportunities. Mechanical Systems and Signal Processing 2009;23:72439.
11. Doebling SW, Hemez FM. Overview of uncertainty assessment for structural health monitoring. In: Proceedings of the 3rd international workshop on structural health monitoring. Stanford University, 2001.
12. WL Oberkampf, JC Helton, CA Joslyn, SF Wojtkiewicz, and S. Ferson Challenge problems: uncertainty in system response given uncertain parameters Reliability Engineering & System Safety 85 2004 11 19
13. P. Walley Statistical reasoning with imprecise probabilities 1991 Chapman and Hall
14. J. Pearl Probabilistic reasoning in intelligent systems: networks of plausible inference 1988 Morgan Kauffmann San Francisco, CA
15. LA. Zadeh Fuzzy sets Information and Control 8 1965 338 353
16. LA. Zadeh Fuzzy sets as a basis for a theory of possibility Fuzzy Sets and Systems 100 1999 9 34
17. G.A Shafer Mathematical theory of evidence 1976 Princeton University Press
18. RR Yager, J Kacprzyk, and M. Fedrizzi Advances in the DempsterShafer theory of evidence 1994 John Wiley & Sons, Inc.
19. I. Bloch Information combination operators for data fusion: a comparative review with classification IEEE Transactions on Systems, Man and Cybernetics, Part A 26 1996 52 67
20. DL Hall, and J. Llinas Handbook of multisensor data fusion 2001 CRC Press LLC
21. RC Luo, Y Chih-Chen, and S. Kuo Lan Multisensor fusion and integration: approaches, applications, and future research directions Sensors Journal, IEEE 2 2002 107 119
22. E Nikolaidis, S Chen, H Cudney, RT Haftka, and R. Rosca Comparison of probability and possibility for design against catastrophic failure under uncertainty Journal of Mechanical Design 126 2004 386 394
23. Ross T, Booker J, Hemez F, Anderson M, Reardon B, Joslyn C. Quantifying total uncertainty using different mathematical theories in a validation assessment. ASCE joint specialty conference on probabilistic mechanics and structural reliability, Albuquerque, NM, 2004.
24. P Soundappan, E Nikolaidis, RT Haftka, R Grandhi, and R. Canfield Comparison of evidence theory and Bayesian theory for uncertainty modeling Reliability Engineering & System Safety 85 2004 295 311
25. D Shepherd, and A. Hollos Reasoning systems for diagnostics and prognostics Aerospace Conference, IEEE 2008 1 11
26. Sarigul-Klijn N. Aeronautical and space engineering materials. In: CRC press TFG (ed). Engineering handbook, 2nd edition, 2004.
27. CR Farrar, and NAJ Lieven Damage prognosis: the future of structural health monitoring Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 2007 623 632
28. Sarigul-Klijn N, Lopez I., Beak SI and Kuo B, "Smart" monitoring of structural health in flight environment (invited keynote paper), 2008. In: Proceedings of the ICCEE, Japan; 2008.
29. G Kerschen, K Worden, AF Vakakis, and J-C. Golinval Past, present and future of nonlinear system identification in structural dynamics Mechanical Systems and Signal Processing 20 2006 505 592
30. JM Nichols, MD Todd, M Seaver, and LN. Virgin Use of chaotic excitation and attractor property analysis in structural health monitoring Physical Review E 67 2003 016209
31. MAF Harrison, S Knudsen, M Seaver, J Nichols, S Trickey, L Pecora, and D. Pecora A comparison of time series analysis algorithms for detection of barely visible impact damage in UAV wings Nondestructive evaluation and health monitoring of aerospace materials, composites, and civil infrastructure V 2006 SPIE San Diego, CA, USA 61760B-61712
32. DE Adams, and M. Nataraju A nonlinear dynamical systems framework for structural diagnosis and prognosis International Journal of Engineering Science 40 2002 1919 1941
33. JN Yang, Y Lei, S Lin, and N. Huang HilbertHuang based approach for structural damage detection Journal of Engineering Mechanics 130 2004 85 95
34. WJ. Staszewski Structural and mechanical damage detection using wavelets The Shock and Vibration Digest 30 1998 457 472
35. M Markou, and S. Singh Novelty detection: a reviewpart 2: neural network based approaches Signal Processing 83 2003 2499 2521
36. N Bakhary, H Hao, and AJ. Deeks Damage detection using artificial neural network with consideration of uncertainties Engineering Structures 29 2007 2806 2815
37. Patton RJ, Chen J, Siew TM. In: proceedings of the international conference on fault diagnosis in nonlinear dynamic systems via neural networks, vol. 1342,1994, p. 134651.
38. SG Pierce, K Worden, and A. Bezazi Uncertainty analysis of a neural network used for fatigue lifetime prediction Mechanical Systems and Signal Processing 22 2008 1395 1411
39. PM Frank, and B. Kppen-Seliger Fuzzy logic and neural network applications to fault diagnosis International Journal of Approximate Reasoning 16 1997 67 88
40. SA Ramu, and VT. Johnson Damage assessment of composite structuresa fuzzy logic integrated neural network approach Computers & Structures 57 1995 491 502
41. X Wu, J Ghaboussi, and JH. Garrett Use of neural networks in detection of structural damage Computers & Structures 42 1992 649 659
42. C Zang, and M. Imregun Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection Journal of Sound and Vibration 242 2001 813 827
43. K. Matsuoka Noise injection into inputs in back-propagation learning IEEE Transactions onSystems, Man and Cybernetics 22 1992 436 440
44. Yang YM, Ge ZX, Xu YC. Fault diagnosis of complex systems based on multi-sensor and multi-domain knowledge information fusion. IEEE international conference on networking, sensing and control, ICNSC 2008, p. 106569.
45. J Ching, JL Beck, and KA. Porter Bayesian state and parameter estimation of uncertain dynamical systems Probabilistic Engineering Mechanics 21 2006 81 96
46. A Smyth, and M. Wu Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring Mechanical Systems and Signal Processing 21 2007 706 723
47. JN Yang, S Lin, H Huang, and L. Zhou An adaptive extended Kalman filter for structural damage identification Structural Control and Health Monitoring 13 2006 849 867
48. JN Yang, N Jann, and S. Lin On-line identification of non-linear hysteretic structures using an adaptive tracking technique International Journal of Non-Linear Mechanics 39 2004 1481 1491
49. LS Katafygiotis, and K-V. Yuen Bayesian spectral density approach for modal updating using ambient data Advances in Structural Engineering 30 2001 1103 1123
50. K-V Yuen, JL Beck, and LS. Katafygiotis Unified probabilistic approach for model updating and damage detection Journal of Applied Mechanics 73 2006 555 564
51. K-V Yuen, and LS. Katafygiotis Substructure identification and health monitoring using noisy response measurements only Computer-Aided Civil and Infrastructure Engineering 21 2006 280 291
52. Muto MM. Application of Stochastic Simulation Methods to System Identification. California Institute of Technology, 2006.
53. G Kerschen, J-C Golinval, and FM. Hemez Bayesian model screening for the identification of nonlinear mechanical structures Journal of Vibration and Acoustics 125 2003 389 397
54. K Wang, DJ Inman, and CR. Farrar Crack-induced changes in divergence and flutter of cantilevered composite panels Structural Health Monitoring 4 2005 377 392
55. H. Sohn Effects of environmental and operational variability on structural health monitoring Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 2007 539 560
56. H Sohn, K Worden, and CR. Farrar Statistical damage classification under changing environmental and operational conditions Journal of Intelligent Material Systems and Structures 13 2002 561 574
57. B Peeters, J Maeck, and GD. Roeck Vibration-based damage detection in civil engineering: excitation sources and temperature effects Smart Materials and Structures 10 2001 518 527
58. Lopez I, Sarigul-Klijn N. Impact damage assessment of time-varying structures under temperature variations. In: proceedings of the 4th european structural health monitoring workshop. Krokaw, Poland, 2008.
59. E Balmes, M Basseville, F Bourquin, L Mevel, H Nasser, and F. Treyssede Merging sensor data from multiple temperature scenarios for vibration monitoring of civil structures Structural Health Monitoring 7 2008 129 142
60. K-Y Koo, S Park, J-J Lee, and C-B. Yun Automated impedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects Journal of Intelligent Material Systems and Structures 20 2009 367 377
61. G Manson, S Pierce, and K. Worden On the long-term stability of normal condition for damage detection in a composite panel Key Engineering Materials 2001 204 205
62. A Raghavan Cesnik CES. Effects of elevated temperature on guided-wave structural health monitoring Intelligent Material Systems and Structures 19 2008 1383 1398
63. Lew J-S. Reduction of uncertainty effect on damage identification using feedback control. Journal of Sound and Vibration 2008;318:90310.
64. P Moyo, and JMW. Brownjohn Application of BoxJenkins models for assessing the effect of unusual events recorded by structural health monitoring systems Structural Health Monitoring 1 2002 149 160
65. N Mehranbod, M Soroush, and C. Panjapornpon A method of sensor fault detection and identification Journal of Process Control 15 2005 321 339
66. MI Friswell, and DJ. Inman Sensor validation for smart structures Journal of Intelligent Material Systems and Structures 10 1999 973 982
67. M Abdelghani, and MI. Friswell Sensor validation for structural systems with multiplicative sensor faults Mechanical Systems and Signal Processing 21 2007 270 279
68. G Kerschen, PD Boe, J-C Golinval, and K. Worden Sensor validation using principal component analysis Smart Materials and Structures 14 2005 36 42
69. PH Ibarguengoytia, LE Sucar, and S. Vadera Real time intelligent sensor validation IEEE Transactions on Power Systems 16 2001 770 775
70. Mengshoel OJ, Darwiche A, Uckun S. Sensor validation using Bayesian networks. in:, proceedings of the ninth international symposium on artificial intelligence, robotics, and automation in space. Los Angeles, CA, 2008.
71. Z Dai, Y Li, B Zheng, and X.A Shen Distributed coordination framework for adaptive sensor uncertainty handling Computational Science ICCS 2007 2007 1171 1174
72. Goebel K. Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques. University of California at Berkeley, 1996.
73. H Guo, W Shi, and Y. Deng Evaluating sensor reliability in classification problems based on evidence theory IEEE Transactions on Systems, Man, and Cybernetics, Part B 36 2006 970 981
74. RF Guratzsch, and S. Mahadevan Sensor Placement for Aerospace Vehicle Health Monitoring Systems Earth & Space 2006 2006 ASCE League City/Houston, TX, USA 2-2
75. WJ Staszewski, and K. Worden Overview of optimal sensor location methods for damage detection Smart Structures and Materials 2001: Modeling, Signal Processing, and Control in Smart Structures 2001 SPIE Newport Beach, CA, USA p. 179187
76. Nagayama T. Structural health monitoring using smart sensors. Civil Engineering: University of Illinois at Urbana-Champaign, 2007.
77. V Giurgiutiu, AN Zagrai, and J. Bao Piezoelectric Wafer Embedded active sensors for aging aircraft structural health monitoring Structural Health Monitoring 1 2002 41 61
78. WJ. Staszewski Advanced data pre-processing for damage identification based on pattern recognition International Journal of Systems Science 31 2000 1381 1396
79. WJ. Staszewski Intelligent signal processing for damage detection in composite materials Composites Science and Technology 62 2002 941 950
80. LI. Kuncheva Combining pattern classifiers: methods and algorithms 2004 Wiley-Interscience
81. AK Jain, RPW Duin, and M. Jianchang Statistical pattern recognition: a review IEEE Transactions on Pattern Analysis and Machine Intelligence 22 2000 4 37
82. Lopez I, Baek S, Sarigul-Klijn N. Application of dimensional reduction techniques to near-field aeroacoustic damage detection. International IEEE Prognostics and Health Management (PHM). Denver, CO, 2008.
83. I Lopez, and N. Sarigul-Klijn Distance similarity matrix using ensemble of dimensional data reduction techniques: vibration and Aeroacoustics case studies Mechanical Systems and Signal Processing 23 2009 2287 2300
84. P Mitra, CA Murthy, and SK. Pal Unsupervised feature selection using feature similarity IEEE Transactions on Pattern Analysis and Machine Intelligence 24 2002 301 312
85. M Markou, and S. Singh Novelty detection: a reviewpart 1: statistical approaches Signal Processing 83 2003 2481 2497
86. H Sohn, DW Allen, K Worden, and CR. Farrar Structural damage classification using extreme value statistics Journal of Dynamic Systems, Measurement, and Control 127 2005 125 132
87. H Sohn, JA Czarnecki, and CR. Farrar Structural health monitoring using statistical process control Journal of Structural Engineering 126 2000 1356 1363
88. Sohn H, Farrar C, Hunter N, Worden K. Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat. LANL technical report 2001.
89. H Sohn, CR Farrar, NF Hunter, and K. Worden Structural health monitoring using statistical pattern recognition techniques Journal of Dynamic Systems, Measurement, and Control 123 2001 706 711
90. H Sohn, and KH.A Law Bayesian probabilistic approach for structure damage detection Earthquake Engineering & Structural Dynamics 26 1997 1259 1281
91. G Niu, J-D Son, A Widodo, B-S Yang, D-H Hwang, and D-S.A Kang Comparison of classifier performance for fault diagnosis of induction motor using multi-type signals Structural Health Monitoring 2007 215 229
92. Cao Y. Bayesian based structural health management and reliability analysis techniques utilizing support vector machine. Ph.D. dissertation in mechanical engineering. North Carolina State University, 2007.
93. Das S, Srivastava AN, Chattopadhyay A. Classification of damage signatures in composite plates using one-class SVMs. In: IEEE (ed). IEEE aerospace conference, 2007.
94. SA Ramu, and VT. Johnson Damage assessment of composite structuresA fuzzy logic integrated neural network approach Computers & Structures 57 1995 491 502
95. E Altunok, MMR Taha, DS Epp, RL Mayes, and TJ Baca Damage pattern recognition for structural health monitoring using fuzzy similarity prescription Computer-Aided Civil and Infrastructure Engineering 21 2006 549 560
96. Y Lei, Z He, Y Zi, and X. Chen New clustering algorithm-based fault diagnosis using compensation distance evaluation technique Mechanical Systems and Signal Processing 22 2008 419 435
97. SG Pierce, K Worden, and G. Manson A novel information-gap technique to assess reliability of neural network-based damage detection Journal of Sound and Vibration 293 2006 96 111
98. M DeSimio, S Olson, and M. Derriso Decision uncertainty in a structural health monitoring system Smart structures and materials 2005: smart structures and integrated systems 2005 SPIE San Diego, CA, USA 530541
99. E Altunok, MMR Taha, and TJ. Ross Possibilistic approach for damage detection in structural health monitoring Journal of Structural Engineering 133 2007 1247 1256
100. Chandrashekhar M, Ganguli R. Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation. Mechanical Systems and Signal Processing 2009;23:384404.
101. MMR Taha, and JL. Lucero A generic fuzzy metric for damage recognition in structural health monitoring systems IEEE International Conference on Systems, Man and Cybernetics 1512 2005 1518 1523
102. S Da Silva, M Dias Jnior, V Lopes Junior, and MJ. Brennan Structural damage detection by fuzzy clustering Mechanical Systems and Signal Processing 22 2008 1636 1649
103. PM Pawar, and R. Ganguli Genetic fuzzy system for damage detection in beams and helicopter rotor blades Computer Methods in Applied Mechanics and Engineering 192 2003 2031 2057
104. D Cayrac, D Dubois, and H. Prade Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application IEEE Transactions on Fuzzy Systems 4 1996 251 269
105. Kang H, Cheng J, Kim I, Vachtsevanos G. An application of fuzzy logic and DempsterShafer theory to failure detection and identification. In: Proceedings of the 30th IEEE conference on decision and control, vol. 1552, 1991, p. 155560
106. Mourelatos ZP, Zhou J. Reliability estimation and design with insufficient data based on possibility theory. AIAA/ISSMO multidisciplinary analysis and optimization conference. Albany, New York, 2004.
107. H Bae, and R. Grandhi Uncertainty quantification of structural response using evidence theory AIAA Journal 2003 41
108. H-R Bae, RV Grandhi, and RA. Canfield Epistemic uncertainty quantification techniques including evidence theory for large-scale structures Computers & Structures 82 2004 1101 1112
109. B-S Yang, and KJ. Kim Application of DempsterShafer theory in fault diagnosis of induction motors using vibration and current signals Mechanical Systems and Signal Processing 20 2006 403 420
110. Y Bao, H Li, and J.A Ou DempsterShafer evidence theory-based approach for online structural health monitoring Health monitoring of structural and biological systems 2008 2008 SPIE San Diego, California, USA 69351T-69310
111. H Guo, and L. Zhang A weighted balance evidence theory for structural multiple damage localization Computer Methods in Applied Mechanics and Engineering 195 2006 6225 6238
112. H Li, Y Bao, and J. Ou Structural damage identification based on integration of information fusion and shannon entropy Mechanical Systems and Signal Processing 22 2008 1427 1440
113. O Basir, and X. Yuan Engine fault diagnosis based on multi-sensor information fusion using DempsterShafer evidence theory Information Fusion 8 2007 379 386
114. T. Denoeux A neural network classifier based on DempsterShafer theory IEEE Transactions on Systems, Man and Cybernetics, Part A 30 2000 131 150
115. CR Parikh, MJ Pont, and N. Barrie Jones Application of DempsterShafer theory in condition monitoring applications: a case study Pattern Recognition Letters 22 2001 777 785
116. T. Dietterich Ensemble methods in machine learning Multiple Classifier Systems 2000 1 15
117. CS Byington, and AK. Garga Data fusion for developing predictive diagnostics for electromechanical systems LD Hall, J Llinas, Handbook of sensor fusion 2000 CRC Press West Palm Beach, FL 23.21 23.31
118. G Vachtsevanos, FL Lewis, M Roemer, A Hess, and B. Wu Intelligent fault diagnosis and prognosis for engineering systems 2006 John Wiley & Sons
119. Byington CS, Watson MJ, Sanket A, Begin M. False alarm mitigation of vibration diagnostic systems. Aerospace conference, 2008 IEEE, 2008, p. 1-11.
120. X Fan, and MJ. Zuo Fault diagnosis of machines based on DS evidence theory. Part 1: DS evidence theory and its improvement Pattern Recognition Letters 27 2006 366 376
121. X Fan, and MJ. Zuo Fault diagnosis of machines based on DS evidence theory. Part 2: Application of the improved DS evidence theory in gearbox fault diagnosis Pattern Recognition Letters 27 2006 377 385 (Pubitemid 43242915)
122. HY. Guo Structural damage detection using information fusion technique Mechanical Systems and Signal Processing 20 2006 1173 1188
123. HY Guo, and ZL. Li A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm Mechanical Systems and Signal Processing 23 2009 769 782
124. Schoeller MH, Roemer MJ, Leonard MS, Derriso M. Embedded reasoning supporting aerospace IVHM. AIAA Infotech Conference and Exhibit. Rohnert Park, CA, 2007.
125. K Worden, G Manson, and T. Denoeux An evidence-based approach to damage location on an aircraft structure Mechanical Systems and Signal Processing 23 2009 1792 1804
126. RA Osegueda, SR Seelam, B Mulupuru, and V. Kreinovich Statistical and DempsterShafer techniques in testing structural integrity of aerospace structures Smart nondestructive evaluation and health monitoring of structural and biological systems II 2003 SPIE San Diego, CA, USA 140-151
127. R. Isermann Model-based fault-detection and diagnosisstatus and applications Annual Reviews in Control 29 2005 71 85
128. RJ Patton, CJ Lopez-Toribio, and FJ. Uppal Artificial intelligence approaches to fault diagnosis IEE Colloquium on Condition Monitoring: Machinery, External Structures and Health (Ref No 1999/034), 1999 511 518
129. Patton RJ, Uppal FJ, Lopez-Toribio CJ. Soft computing approaches to fault diagnosis for dynamic systems: a survey. In: Proceedings of the fourth IFAC symposium on fault detection supervision and safety, 2000.
130. V Venkatasubramanian, R Rengaswamy, and SN. Kavuri A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies Computers & Chemical Engineering 27 2003 313 326 (Pubitemid 36285963)
131. V Venkatasubramanian, R Rengaswamy, SN Kavuri, and K. Yin A review of process fault detection and diagnosis: Part III: Process history based methods Computers & Chemical Engineering 27 2003 327 346 (Pubitemid 36285964)
132. V Venkatasubramanian, R Rengaswamy, K Yin, and SN. Kavuri A review of process fault detection and diagnosis: Part I: quantitative model-based methods Computers & Chemical Engineering 27 2003 293 311 (Pubitemid 36285962)
133. CS Byington, and MJ. Roemer Selected prognostic methods with application to an integrated health management system SG Tzafestas, Applications of intelligent control to engineering systems 2009 Springer Netherlands 3 21
134. PM Frank, and B. Kppen-Seliger New developments using AI in fault diagnosis Engineering Applications of Artificial Intelligence 10 1997 3 14
135. Roemer M, Kacprzynski G, Orsagh R. Assessment of data and knowledge fusion strategies for prognostics and health management. IEEE Aerospace Conference Proceedings, 2001.
136. D Chelidze, and JP.A Cusumano Dynamical Systems Approach to Failure Prognosis Journal of Vibration and Acoustics 126 2004 2 8
137. G Niu, and B-S. Yang DempsterShafer regression for multi-step-ahead time-series prediction towards data-driven machinery prognosis Mechanical Systems and Signal Processing 23 2009 740 751
138. Hess A, Calvello G, Frith P. Challenges, issues, and lessons learned chasing the "Big P". Real predictive prognostics. Part 1. In: Aerospace conference, IEEE, 2005, p. 361019.
139. Hess A, Calvello G, Frith P, Engel SJ, Hoitsma D. Challenges, issues, and lessons learned chasing the big p: real predictive prognostics part 2. In: Aerospace conference, IEEE, 2006, p. 119.
140. Engel SJ, Gilmartin BJ, Bongort K, Hess A. Prognostics, the real issues involved with predicting life remaining. In: Aerospace conference proceedings, IEEE, vol. 456, 2000. p. 45769.
141. Schwabacher M. A Survey of data-driven prognostics. In: AIAA Infotech@ aerospace conference. Arlington, VA, 2005.
142. Y Zhang, and J. Jiang Bibliographical review on reconfigurable fault-tolerant control systems Annual Reviews in Control 32 2008 229 252
143. N. Sarigul-Klijn, R Rapetti, A Jordan, I Lopez, P Nespeca, and M Sarigul-Klijn Intelligent Trajectory Generation for a Distressed Aircraft AIAA Journal of Aircraft 47 2010 1 2
144. M Blanke, M Kinnaert, J Lunze, and M. Staroswiecki Diagnosis and fault-tolerant control 2001 Springer-Verlag
145. R. Isermann On fuzzy logic applications for automatic control, supervision, and fault diagnosis IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 28 1998 221 235
146. H Fang, H Ye, and M. Zhong Fault diagnosis of networked control systems Annual Reviews in Control 31 2007 55 68
147. H Noura, D Theilliol, JC Ponsart, and A. Chamseddine Fault-tolerant control systems 2009 Springer-Verlag
148. G. Ducard Fault-tolerant flight control and guidance systems: practical methods for small unmanned aerial vehicles 2009 Springer-Verlag
149. Niemann H, Stoustrup J. Integration of control and fault detection: nominal and robust design. In: Proceedings of the IFAC Symposium on fault detection, 1999.
150. PJ Antsaklis, and KM. Passino An introduction to intelligent and autonomous control 1992 Kluwer Academic Norwell, MA
151. RF. Stengel Toward intelligent flight control IEEE Transactions on Systems, Man and Cybernetics, 23 1993 1699 1717
152. M Bodson, and JE. Groszkiewicz IEEE Transactions on multivariable adaptive algorithms for reconfigurable flight control Control Systems Technology, 5 1997 217 229
153. RW. Brennan Toward real-time distributed intelligent control: a survey of research themes and applications IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37 2007 744 765
154. Y Diao, and KM. Passino Intelligent fault-tolerant control using adaptive and learning methods Control Engineering Practice 10 2002 801 817
155. Lin CL, Su H-W. Intelligent control theory in guidance and control system design: an overview. In: Proceedings of the national science council, Republic of China, part a: physical science and engineering, vol. 24, 2000, p. 1530.
156. KM Passino, and S. Yurkovich Fuzzy control 1998 Addison-Wesley Reading, MA
157. WA Kwong, KM Passino, EG Laukonen, and S. Yurkovich Expert supervision of fuzzy learning systems for fault tolerant aircraft control Proceedings of the IEEE 83 1995 466 483
158. S. LaValle Planning algorithms 2006 Cambridge University Press 〈also available at http://msl.cs.uiuc.edu/planning/
159. G.A Feng Survey on analysis and design of model-based fuzzy control systems IEEE Transactions on Fuzzy Systems 14 2006 676 697
160. H. Hassan KL Priddy, Intelligent control system using DempsterShafer theory of evidence. 2004 SPIE Orlando, FL, USA 38 47
161. Oosterom M, Babuska R. Virtual sensor for fault detection and isolation in flight control systemsfuzzy modeling approach. In: Proceedings of the 39th IEEE conference on decision and control, vol. 2643, 2000, p. 264550.
162. R Seplveda, O Castillo, P Melin, A Rodrguez-Daz, and O. Montiel Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic Information Sciences 177 2007 2023 2048
163. Bateman F, Noura H, Ouladsine M. Actuators fault diagnosis and tolerant control for an unmanned aerial vehicle. in: IEEE international conference on control applications, CCA, 2007, p. 106166.
164. Boskovic JD, Bergstrom SE, Mehra RK. Retrofit reconfigurable flight control in the presence of control effector damage. In: Proceedings of theAmerican Control Conference, vol. 2654, 2005, p. 265257
165. JD Boskovic, SE Bergstrom, and RK. Mehra Robust integrated flight control design under failures, damage, and state-dependent disturbances Journal of Guidance, Control, and Dynamics 28 2005 902 917
166. A Marcos, S Ganguli, and GJ. Balas An application of fault detection and isolation to a transport aircraft Control Engineering Practice 13 2005 105 119
167. Groszkiewicz JE, Bodson M. Flight control reconfiguration using adaptive methods, In: Proceedings of the 34th IEEE conference on decision and control, vol. 1152, 1995, p. 115964.
168. Herzallah R. Uncertainty in control problems: a survey. international federation of automatic control (IFAC). Prague, Czech Republic, 2005.
169. Zang TA, Hemsch MJ, Hilburger MW, Kenny SP, Luckring JM, Maghami P, Padula SL, Stroud WJ. needs and opportunities for uncertainty-based multidisciplinary design methods for aerospace vehicles. NASA Langley Research Center, 2002.
170. Z Gong, C Wen, and DP. Mital Decentralized robust controller design for a class of interconnected uncertain systems: with unknown bound of uncertainty IEEE Transactions on Automatic Control 41 1996 850 854
171. Belcastro CM, Khong TH, Shin J-Y, Kwatny H, Chang B-C, Balas GJ. Uncertainty modeling for robustness analysis of aircraft control upset prevention and recovery systems. AIAA guidance, navigation, and control conference and exhibit. San Francisco, California, 2005.
172. Crespo LG. Optimal performance, robustness and reliability based designs of systems with structured uncertainty. In: Proceedings of the American control conference, vol. 4215, 2003, p. 4219224.
173. Mahmoud M. Stability analysis of discrete fault tolerant control systems with parameter uncertainties. In: American Control Conference, 2008, p. 105863.
174. Z Qu, CM Ihlefeld, Y Jin, and A. Saengdeejing Robust fault-tolerant self-recovering control of nonlinear uncertain systems Automatica 39 2003 1763 1771
175. Yang H, Jiang B, Staroswiecki M. Supervisory fault tolerant control for a class of uncertain nonlinear systems. Automatica 2009;45:231924.
176. X Zhang, T Parisini, and MM. Polycarpou Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach IEEE Transactions on Automatic Control 49 2004 1259 1274
177. SN Singh, ML Steinberg, and AB. Page Nonlinear adaptive and sliding mode flight path control of F/A-18 model IEEE Transactions on Aerospace and Electronic Systems 39 2003 1250 1262
178. EM Jafarov, and R. Tasaltin Robust sliding-mode control for the uncertain MIMO aircraft model F-18 IEEE Transactions on Aerospace and Electronic Systems 36 2000 1127 1141
179. Khammash M, Zou L, Almquist JA, Van Der Linden C. Robust aircraft pitch-axis control under weight and center of gravity uncertainty. In: Proceedings of the 38th IEEE Conference on decision and control, vol. 1972, 1999. p. 197075.
180. Sarigul-Klijn N, Nespeca P, Marchelli T, Sarigul-Klijn M. An approach to predict flight dynamics and stability derivatives of distressed aircraft. In: AIAA atmospheric flight mechanics conference and exhibit. Honolulu, HI, 2008.
181. Ganguli S, Papageorgiou G, Glavaski S. Aircraft fault detection, isolation and reconfiguration in the presence of measurement errors. In: AIAA guidance, navigation, and control conference and exhibit. Keystone, Colorado, 2006.
182. VV Patel, C Cao, N Hovakimyan, KA Wise, and E. Lavretsky L1 adaptive controller for tailless unstable aircraft in the presence of unknown actuator failures International Journal of Control 82 2009 705 720
183. Hallouzi R, Verhaegen M. Reconfigurable fault tolerant control of a boeing 747 using subspace predictive control. In: AIAA guidance, navigation and control conference and exhibit. Hilton Head, South Carolina, 2007.
184. DK Tolani, A Ray, and JF. Horn Integrated decision and control of human-engineered complex systems International Journal of General Systems 35 2006 275 294
185. Rong H-J, Guang-Bin H, Sundararajan N, Saratchandran P. Fuzzy fault tolerant controller for actuator failures during aircraft autolanding. In: IEEE international conference on fuzzy systems, 2006: 120004.
186. H-J Rong, N Sundararajan, P Saratchandran, and H. Guang-Bin Adaptive fuzzy fault-tolerant controller for aircraft autolanding under failures IEEE Transactions on Aerospace and Electronic Systems 43 2007 1586 1603 (Pubitemid 351344377)
187. CL. Pettit Uncertainty quantification in aeroelasticity: Recent results and research challenges Journal of Aircraft 41 2004 1217 1229
188. SR Hunt, and IG. Hebden Validation of the Eurofighter Typhoon structural health and usage monitoring system Smart Materials and Structures 2001 10
189. Jordan T, Langford W, Hil J. Airborne Subscale transport aircraft research testbedaircraft model development. AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005.
190. David V. Automatic supervisory adaptive control. Rockwell Collins Case Study: Damage Tolerance 〈 http://www.rockwellcollins.com/athena/demos/ damage-tolerance/ 〉, 2008.