Sensor Fault Detection and Isolation Using System Dynamics Identification Techniques.

[1]  C.S.G. Lee,et al.  Fusion-based sensor fault detection , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[2]  S. Billings,et al.  VARIABLE SELECTION IN NON-LINEAR SYSTEMS MODELLING , 1999 .

[3]  A. Komori,et al.  Diagnosis of instrument fault , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[4]  Mattias Nyberg,et al.  Using hypothesis testing theory to evaluate principles for leakage diagnosis of automotive engines , 2003 .

[5]  Qing-Guo Wang,et al.  An extended self-organizing map for nonlinear system identification , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[6]  Rik Pintelon,et al.  Modified AIC and MDL model selection criteria for short data records , 2004, IEEE Transactions on Instrumentation and Measurement.

[7]  Carlos Canudas-de-Wit,et al.  Robust strategy for intake leakage detection in diesel engines , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[8]  Robert Sedgewick,et al.  Algorithms in C : Part 5 : Graph Algo-rithms , 2002 .

[9]  Wu-chun Feng,et al.  Winner take all experts network for sensor validation , 2000, Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162).

[10]  K. Mathioudakis,et al.  Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults , 2002 .

[11]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[12]  Antonio Pietrosanto,et al.  On-line sensor fault detection, isolation, and accommodation in automotive engines , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[13]  Josef Havel,et al.  Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks , 2006 .

[14]  John B. Heywood,et al.  Internal combustion engine fundamentals , 1988 .

[15]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[16]  Belle R. Upadhyaya,et al.  Generalized consistency checking of multivariable redundant measurements and common-mode failure detection , 1989 .

[17]  R. N. Claek.  Instrument Fault Detection , 1978 .

[18]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[19]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[20]  Weihua Li,et al.  Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .

[21]  Jan Lunze,et al.  Sensor and actuator fault diagnosis of systems with discrete inputs and outputs , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Sunil Vadera,et al.  Real Time Intelligent Sensor Validation , 2001 .

[23]  Donald L. Simon,et al.  A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics , 2005 .

[24]  Ethan A. Scarl,et al.  Diagnosis and Sensor Validation through Knowledge of Structure and Function , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[26]  P. Frank,et al.  Sensitivity Discriminating Observer Design for Instrument Failure Detection , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[27]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[28]  Jacek M. Zurada,et al.  Input selection in data-driven fuzzy modeling , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[29]  Xiaowen Fang,et al.  Detection and Diagnosis of Plant Failures: The Orthogonal Parity Equation Approach , 1990 .

[30]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[31]  Antonio Pietrosanto,et al.  A neural network approach to instrument fault detection and isolation , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[32]  Hong Guo,et al.  Automotive signal fault diagnostics - part I: signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection , 2003, IEEE Trans. Veh. Technol..

[33]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[34]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[35]  Frank Allgöwer,et al.  Constrained derivative-free augmented state estimation for a diesel engine air path , 2006 .

[36]  G. Strang.  Introduction to Linear Algebra , 1993 .

[37]  D. M. Himmelblau,et al.  Instrument fault detection in systems with uncertainties , 1982 .

[38]  Janos Gertler,et al.  A new structural framework for parity equation-based failure detection and isolation , 1990, Autom..

[39]  Martyn G. Ford,et al.  Unsupervised Forward Selection: A Method for Eliminating Redundant Variables , 2000, J. Chem. Inf. Comput. Sci..

[40]  Kihoon Choi,et al.  Application of an Effective Data-Driven Approach to Real-time time Fault Diagnosis in Automotive Engines , 2007, 2007 IEEE Aerospace Conference.

[41]  Ann E. Nicholson,et al.  Dynamic Belief Networks for Discrete Monitoring , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[42]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[43]  Giorgio Rizzoni,et al.  A Survey of Automotive Diagnostic Equipment and Procedures , 1993 .

[44]  Mark A. Kramer,et al.  Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes , 1993 .

[45]  Antonio Pietrosanto,et al.  An advanced neural-network-based instrument fault detection and isolation scheme , 1998, IEEE Trans. Instrum. Meas..

[46]  Stephen A. Billings,et al.  Radial Basis Function Network Configuration Using Mutual Information and the Orthogonal Least Squares Algorithm , 1996, Neural Networks.

[47]  Barry M. Wise,et al.  RECENT ADVANCES IN MULTIVARIATE STATISTICAL PROCESS CONTROL: IMPROVING ROBUSTNESS AND SENSITIVITY , 1991 .

[48]  M. Tomizuka,et al.  Sensor fault detection in vehicle lateral control systems via switching Kalman filtering , 2005, Proceedings of the 2005, American Control Conference, 2005..

[49]  Fredrik Nilsson.  Diagnosis of a Truck Engine using Nolinear Filtering Techniques , 2007 .

[50]  T. A. Brownell.  Neural networks for sensor management and diagnostics , 1992, Proceedings of the IEEE 1992 National Aerospace and Electronics Conference@m_NAECON 1992.

[51]  Erkki Oja,et al.  Modified Hebbian learning for curve and surface fitting , 1992, Neural Networks.

[52]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[53]  Giorgio Rizzoni,et al.  Detection of sensor failures in automotive engines , 1991 .

[54]  Martin Gunnarsson,et al.  Parameter Estimation for Fault Diagnosis of an Automotive Engine using Extended Kalman Filters , 2001 .

[55]  H. Akaike.  A new look at the statistical model identification , 1974 .

[56]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[57]  R. Ocampo-Pérez,et al.  Adsorption of Fluoride from Water Solution on Bone Char , 2007 .

[58]  Ole J. Mengshoel,et al.  Sensor Validation using Bayesian Networks , 2008 .

[59]  Fu Xiao,et al.  AHU sensor fault diagnosis using principal component analysis method , 2004 .

[60]  Mattias Nyberg,et al.  Model-based diagnosis of an automotive engine using several types of fault models , 2002, IEEE Trans. Control. Syst. Technol..

[61]  Frédéric Kratz,et al.  Sensor fault detection using fuzzy logic and neural networks , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[62]  K. Mathioudakis,et al.  Probabilistic neural networks for validation of on-board jet engine data , 2004 .

[63]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

[64]  Robert Broen.  A nonlinear voter-estimator for redundant systems , 1974, CDC 1974.

[65]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[66]  Antonio Pietrosanto,et al.  Instrument fault detection and isolation: state of the art and new research trends , 2000, IEEE Trans. Instrum. Meas..

[67]  T. McAvoy,et al.  Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .

[68]  A. Negiz,et al.  On the Detection of Multiple Sensor Abnormalities in Multivariate Processes , 1992, 1992 American Control Conference.

[69]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[70]  P. R. Spina,et al.  Reliability in the determination of gas turbine operating state , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[71]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[72]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[73]  Riccardo Scattolini,et al.  Modeling and identification of an electromechanical internal combustion engine throttle body , 1997 .

[74]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[75]  S. Billings,et al.  Piecewise linear identification of non-linear systems , 1987 .

[76]  Alice M. Agogino,et al.  A methodology for intelligent sensor measurement, validation, fusion, and fault detection for equipment monitoring and diagnostics , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[77]  S. Qin,et al.  Self-validating inferential sensors with application to air emission monitoring , 1997 .

[78]  George P. Richardson,et al.  Threshold setting and the cycling of a decision threshold , 2006 .

[79]  Giampiero Campa,et al.  Neural Networks-Based Sensor Validation for the Flight Control System of a B 777 Research Model , 2002 .

[80]  Robert E. Uhrig,et al.  Use of Autoassociative Neural Networks for Signal Validation , 1998, J. Intell. Robotic Syst..

[81]  S. C. Lee,et al.  Sensor value validation based on systematic exploration of the sensor redundancy for fault diagnosis KBS , 1994, IEEE Trans. Syst. Man Cybern..

[82]  D. Himmelblau,et al.  Sensor fault detection via multiscale analysis and nonparametric statistical inference , 1998 .

[83]  Frédéric Kratz,et al.  Detection, isolation, and identification of sensor faults in nuclear power plants , 1996, IEEE Trans. Control. Syst. Technol..

[84]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems via State Estimation - a Survey , 1987 .

[85]  Robert L. Mason,et al.  Regression Analysis and Its Application: A Data-Oriented Approach. , 1982 .

[86]  Aluizio F. R. Araújo,et al.  Identification and control of dynamical systems using the self-organizing map , 2004, IEEE Transactions on Neural Networks.

[87]  T.-H. Guo,et al.  Sensor failure detection and recovery by neural networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[88]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

[89]  Ganesh Krishnamoorthy.  A Framework for Utilizing Data from Multiple Sensors in Intelligent Mechanical Systems , 2010 .

[90]  K.Z. Mao,et al.  Orthogonal forward selection and backward elimination algorithms for feature subset selection , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[91]  Masoud Soroush,et al.  A method of sensor fault detection and identification , 2005 .

[92]  Lennart Ljung,et al.  Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms , 2002, Autom..

[93]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[94]  E. Alcorta Garcia,et al.  A novel design of structured observer-based residuals for FDI , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[95]  Lars Nielsen,et al.  MODEL BASED DIAGNOSIS FOR THE AIR INTAKE SYSTEM OF THE SI-ENGINE , 1997 .

[96]  E. Rhodes,et al.  XXXIV. On lines and planes of closest fit , 1927 .

[97]  G. Betta,et al.  A knowledge-based approach to instrument fault detection and isolation , 1995 .

[98]  Jun Ni,et al.  Time-Frequency Based Sensor Fusion in the Assessment and Monitoring of Machine Performance Degradation , 2002 .

[99]  Yi Lu Murphey,et al.  Automotive fault diagnosis - part II: a distributed agent diagnostic system , 2003, IEEE Trans. Veh. Technol..

[100]  Jun Ni,et al.  Growing Structure Multiple Model Systems for Anomaly Detection and Fault Diagnosis , 2009 .

[101]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[102]  Christine M. Anderson-Cook,et al.  A Quick Course in Statistical Process Control , 2008 .

[103]  V. Walton,et al.  Detecting Instrument Malfunctions in Control Systems , 1975, IEEE Transactions on Aerospace and Electronic Systems.

[104]  Janos Gertler,et al.  Principal Component Analysis and Parity Relations - A Strong Duality , 1997 .

[105]  David M. Himmelblau,et al.  Sensor Fault Detection via Multiscale Analysis and Dynamic PCA , 1999 .

[106]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[107]  B. M. Wise,et al.  UPSET AND SENSOR FAILURE DETECTION IN MULTIVARIATE PROCESSES , 1989 .

[108]  Janos Gertler,et al.  Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions , 2000 .

[109]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[110]  Jun Ni,et al.  The improvement of thermal error modeling and compensation on machine tools by CMAC neural network , 1996 .

[111]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[112]  B. R. Upadhyaya,et al.  Modified noise analysis method for the estimation of temperature sensor response time characteristics , 1978 .

[113]  Yuhua Li,et al.  A review of condition monitoring and fault diagnosis for diesel engines , 2000 .

[114]  Alan S. Willsky,et al.  F-8 DFBW sensor failure identification using analytic redundancy , 1977 .

[115]  E. L. Harder,et al.  The Institute of Electrical and Electronics Engineers, Inc. , 2019, 2019 IEEE International Conference on Software Architecture Companion (ICSA-C).

[116]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[117]  M. Deistler,et al.  On the Impact of Weighting Matrices in Subspace Algorithms , 2000 .

[118]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[119]  Keith J. Burnham,et al.  Improved SI engine modelling techniques with application to fault detection , 2002, Proceedings of the International Conference on Control Applications.

[120]  Fabio A. González,et al.  An immunity-based technique to characterize intrusions in computer networks , 2002, IEEE Trans. Evol. Comput..

[121]  Sergio Bittanti,et al.  High-accuracy fit of the poles of spectroscopy amplifiers designed for mixed analog-digital filtering , 1997 .

[122]  T.-H. Guo,et al.  Neural network based sensor validation for reusable rocket engines , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[123]  Gaëtan Kerschen,et al.  Sensor validation using principal component analysis , 2005 .

[124]  Anna L. Buczak,et al.  Neural-networks-based sensor validation and recovery methodology for advanced aircraft engines , 2001, SPIE Defense + Commercial Sensing.

[125]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[126]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[127]  Josep M. Oller,et al.  Hypothesis testing: a model selection approach , 2002 .

[128]  Antonio Pietrosanto,et al.  The use of genetic algorithms for advanced instrument fault detection and isolation schemes , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[129]  Ali Cinar,et al.  Monitoring sensor performance in multivariable continuous processes , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[130]  Michael J. Piovoso,et al.  Probabilistic model for sensor fault detection and identification , 2003 .

[131]  Yinghua Lin,et al.  Input variable identification - fuzzy curves and fuzzy surfaces , 1996, Fuzzy Sets Syst..

[132]  B.K.N. Rao,et al.  Handbook of Condition Monitoring , 1996 .

[133]  R. Clark.  A Simplified Instrument Failure Detection Scheme , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[134]  Janos Gertler,et al.  Generating directional residuals with dynamic parity relations , 1995, Autom..

[135]  Silvio Simani,et al.  Diagnosis techniques for sensor faults of industrial processes , 2000, IEEE Trans. Control. Syst. Technol..

[136]  Sunil Vadera,et al.  A Probabilistic Model for Information and Sensor Validation , 2006, Comput. J..

[137]  Jianbo Liu,et al.  Topology Preservation and Cooperative Learning in Identification of Multiple Model Systems , 2008, IEEE Transactions on Neural Networks.

[138]  T. Hastie,et al.  Principal Curves , 2007 .

[139]  Babu Joseph,et al.  Sensor Fault Detection Using Noise Analysis , 2000 .

[140]  Sergio M. Savaresi,et al.  Poles identification of an analog filter for nuclear spectroscopy via subspace-based techniques , 2000, IEEE Trans. Control. Syst. Technol..

[141]  van Schrick.  A comparison of IFD schemes: a decision aid for designers , 1994 .

[142]  George C. Verghese,et al.  Optimally robust redundancy relations for failure detection in uncertain systems , 1986, Autom..

[143]  Asok Ray,et al.  A Fault Detection and Isolation Methodology Theory and Application , 1984, 1984 American Control Conference.

[144]  R. P. McDonald,et al.  A second generation nonlinear factor analysis , 1983 .

[145]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[146]  Mario Innocenti,et al.  Sensor validation using hardware-based on-line learning neural networks , 1998 .

[147]  J. Gower,et al.  Methods for statistical data analysis of multivariate observations , 1977, A Wiley publication in applied statistics.

[148]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[149]  A. Johansson,et al.  Parametric uncertainty in sensor fault detection for turbofan jet engine , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[150]  Javad Mohammadpour,et al.  A survey on diagnostic methods for automotive engines , 2012, Proceedings of the 2011 American Control Conference.

[151]  B. Chandrasekaran,et al.  Hierarchical classification: Its usefulness for diagnosis and sensor validation , 1987 .

[152]  Belle R. Upadhyaya,et al.  Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis , 2001 .

[153]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[154]  Gregg H. Gunsch,et al.  An artificial immune system architecture for computer security applications , 2002, IEEE Trans. Evol. Comput..

[155]  S. Mukhopadhyay,et al.  Sensor fault detection and isolation using artificial neural networks , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[156]  M. Verhaegen,et al.  A fast, recursive MIMO state space model identification algorithm , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[157]  J. Chen,et al.  Detecting incipient sensor faults in flight control systems , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[158]  Ya Xiong Zhang,et al.  Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. , 2007, Talanta.

[159]  Pulak Halder,et al.  On line sensor fault detection, isolation and accommodation in tactical aerospace vehicle , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[160]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[161]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[162]  Ian D. Walker,et al.  Diagnosis of automotive electronic throttle control systems , 2004 .

[163]  Lars Nielsen,et al.  Model Based Diagnosis of Leaks in the Air Intake System of an SI-Engine , 1998 .

[164]  Ehsan Mesbahi.  An intelligent sensor validation and fault diagnostic technique for marine diesel engines , 2001 .

[165]  Simon Haykin,et al.  Local Dynamic Modeling with SelfOrganizing Maps and Applications to Nonlinear System Identification and Control , 2001 .

[166]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[167]  Yisong Dai,et al.  A local model on-line sensor fault detection method in the automotive environment , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[168]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[169]  Javad Mohammadpour,et al.  A survey on diagnostics methods for automotive engines , 2011, ACC.

[170]  Keith E. Holbert,et al.  An integrated signal validation system for nuclear power plants , 1990 .

[171]  Sirish L. Shah,et al.  Structured residual vector-based approach to sensor fault detection and isolation , 2002 .

[172]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[173]  Akira Ohsumi,et al.  Identification of Continuous-time Time-varying Stochastic Systems via Distribution-based Approach , 2002 .

[174]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[175]  T. Jeinsch,et al.  Embedded model-based fault diagnosis for on-board diagnosis of engine control systems , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[176]  C. M. Crowe,et al.  Data reconciliation — Progress and challenges , 1996 .

[177]  David Antory,et al.  Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study , 2007 .

[178]  Zdzislaw Kowalczuk,et al.  Model based diagnosis for automotive engines-algorithm development and testing on a production vehicle , 1995, IEEE Trans. Control. Syst. Technol..

[179]  Gang Chen,et al.  Predictive on-line monitoring of continuous processes , 1998 .

[180]  Hongwei Tong,et al.  Detection of gross erros in data reconciliation by principal component analysis , 1995 .

[181]  Mattias Nyberg,et al.  Model based diagnosis of the air path of an automotive diesel engine , 2001 .

[182]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[183]  Hugh F. Durrant-Whyte,et al.  The detection of faults in navigation systems: a frequency domain approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[184]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[185]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[186]  J. Príncipe,et al.  Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control , 1998, Proc. IEEE.

[187]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..