Robust fault diagnosis by GA optimisation with applications to wind turbine systems and induction motors

This investigation focuses and analyses the theoretical and practical performance of a dynamic system, which affords condition monitoring and robust fault diagnosis. The importance of robustness in fault diagnosis is becoming significant for controlled dynamic systems in order to improve operating reliability, critical-safety and reducing the cost often caused by interruption shut down and component repairing. There is a strong motivation to develop an effective real-time monitoring and fault diagnosis strategy so as to ensure a timely response by supervisory personnel to false alarms and damage control due to faults/malfunctions. Environmental disturbances/noises are unavoidable in practical engineering systems, the effects of which usually reduce the diagnostic ability of conventional fault diagnosis algorithms, and even cause false alarms. As a result, robust fault diagnosis is vital for practical application in control systems, which aims to maximize the fault detectability and minimize the effects of environment disturbances/noises. In this study, a genetic algorithm (GA) optimization model-based fault diagnosis algorithm is investigated for applications in wind turbine energy systems and induction motors through concerns for typical types of developing (incipient) and sudden (abrupt) faults. A robust fault detection approach is utilized by seeking an optimal observer gain when GA optimisation problems become solvable so that the residual is sensitive to the faults, but robust against environmental disturbances/noises. Also, robust fault estimation techniques are proposed by integrating augmented observer and GA optimisation techniques so that the estimation error dynamics have a good robustness against environmental disturbances/noises. The two case studies investigated in this project are: a 5MW wind turbine model where robust fault detection and robust fault estimation are discussed with details; and a 2kW induction motor experimental setup is investigated, where robust fault detection and robust fault estimation are both examined, and modelling errors are effectively attenuated by using the proposed algorithms. The simulations and experimental results have demonstrated the effectiveness of the proposed fault diagnosis methods.

[1]  Paul M. Frank,et al.  Analytical and Qualitative Model-based Fault Diagnosis - A Survey and Some New Results , 1996, Eur. J. Control.

[2]  Harold Lee Jones,et al.  Failure detection in linear systems , 1973 .

[3]  Jie Chen,et al.  Robust residual generation for model-based fault diagnosis of dynamic systems. , 1995 .

[4]  Youmin Zhang,et al.  Active fault-tolerant control system against partial actuator failures , 2002 .

[5]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[6]  J. Mcgowan,et al.  Wind Energy Explained , 2002 .

[7]  Z. Blivband,et al.  Expanded FMEA (EFMEA) , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.

[8]  George W. Irwin,et al.  Disturbance attenuation in linear systems via dynamical compensators-a parametric eigenstructure assignment approach , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[9]  D. Luenberger,et al.  Performance-adaptive renewal policies for linear systems , 1969 .

[10]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[11]  J. O'Reilly,et al.  On eigenstructure assignment in linear multivariable systems , 1982 .

[12]  I. Jaimoukha,et al.  Amatrix factorization solution to the H − / H ∞ fault detection problem , 2006 .

[13]  Damien Koenig,et al.  Unknown input proportional multiple-integral observer design for linear descriptor systems: application to state and fault estimation , 2005, IEEE Transactions on Automatic Control.

[14]  Guo-Ping Liu,et al.  Robust control design via eigenstructure assignment, genetic algorithms and gradient-based optimisation , 1994 .

[15]  Mohamed Benbouzid,et al.  Bibliography on induction motors faults detection and diagnosis , 1999 .

[16]  R. Patton,et al.  Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[17]  Qing-Long Han,et al.  Parity Space-Based Fault Estimation for Linear Discrete Time-Varying Systems , 2010, IEEE Transactions on Automatic Control.

[18]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[19]  Marcel Staroswiecki,et al.  Progressive accommodation of parametric faults in linear quadratic control , 2007, Autom..

[20]  M. B. Zarrop,et al.  Sliding Mode Observers for Robust Sensor Monitoring , 1996 .

[21]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[22]  Huaguang Zhang,et al.  A framework of robust fault estimation observer design for continuous‐time/discrete‐time systems , 2013 .

[23]  Jesus Lopez.  Doubly Fed Induction Machine , 2011 .

[24]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

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

[26]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[27]  F.W. Fuchs,et al.  Voltage Sensor Fault Detection and Reconfiguration for a Doubly Fed Induction Generator , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[28]  J. Lam,et al.  Robust Fault Detection Observer Design: Iterative LMI Approaches , 2007 .

[29]  Peng Jun,et al.  Fault detection using unknown input observers for heavy-haul trains , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[30]  Vadim I. Utkin,et al.  Sliding Modes and their Application in Variable Structure Systems , 1978 .

[31]  L. Fridman,et al.  Higher‐order sliding‐mode observer for state estimation and input reconstruction in nonlinear systems , 2008 .

[32]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[33]  Peter Fogh Odgaard,et al.  Observer Based Detection of Sensor Faults in Wind Turbines , 2009 .

[34]  C. Favre,et al.  Fly-by-wire for commercial aircraft: the Airbus experience , 1994 .

[35]  Hong Wang,et al.  Discrete-time proportional and integral observer and observer-based controller for systems both with unknown input and output disturbances , 2007 .

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

[37]  D. C. Hill.  Reduced order modelling of gas turbine engines , 1994 .

[38]  S. Hashmi.  Failure modes and effects analysis through knowledge modelling , 2015 .

[39]  F. R. Salmasi,et al.  An Adaptive Observer With Online Rotor and Stator Resistance Estimation for Induction Motors With One Phase Current Sensor , 2011, IEEE Transactions on Energy Conversion.

[40]  Jie Chen,et al.  Robust fault detection of jet engine sensor systems using eigenstructure assignment , 1991 .

[41]  C. T. Pi,et al.  Simultaneous disturbance attenuation and fault detection using proportional integral observers , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[42]  D. Henry,et al.  Design and analysis of robust residual generators for systems under feedback control , 2005, Autom..

[43]  Jie Chen,et al.  Optimal unknown input distribution matrix selection in robust fault diagnosis , 1993, Autom..

[44]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[45]  M. Silverman,et al.  FMEA on FMEA , 2013, 2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS).

[46]  G. Ault,et al.  Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters , 2008 .

[47]  David Charles Hill.  Identification of Gas Turbine Dynamics: Time-Domain Estimation Problems , 1997 .

[48]  C. Evans.  Testing and modelling aircraft gas turbines: an introduction and overview , 1998 .

[49]  Jie Chen,et al.  OPTIMAL SELECTION OF UNKNOWN INPUT DISTRIBUTION MATRIX IN THE DESIGN OF ROBUST OBSERVERS FOR FAULT DIAGNOSIS , 1991 .

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

[51]  N. Viswanadham,et al.  Actuator fault detection and isolation in linear systems , 1988 .

[52]  Peter J. Fleming,et al.  DYNAMIC MODELLING FOR CONDITION MONITORING OF GAS TURBINES: GENETIC ALGORITHMS APPROACH , 2005 .

[53]  R. Katebi,et al.  Model-based fault detection and isolation for wind turbine , 2012, Proceedings of 2012 UKACC International Conference on Control.

[54]  Zabih Ghassemlooy,et al.  Simulation Study of Fault Detection and Diagnosis for Wind Turbine System , 2014 .

[55]  Wenxian Yang,et al.  Cost-Effective Condition Monitoring for Wind Turbines , 2010, IEEE Transactions on Industrial Electronics.

[56]  Marios M. Polycarpou.  Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach , 2001, IEEE Trans. Autom. Control..

[57]  Emilia Fridman,et al.  Sampled-data sliding mode observer for robust fault reconstruction: A time-delay approach , 2014, J. Frankl. Inst..

[58]  Ron J. Patton,et al.  An application of eigenstructure assignment to robust residual design for FDI , 1996 .

[59]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[60]  Jie Chen,et al.  On eigenstructure assignment for robust fault diagnosis , 2000 .

[61]  Zhiwei Gao,et al.  Robust fault estimation approach and its application in vehicle lateral dynamic systems , 2007 .

[62]  Peter J. Fleming,et al.  On evolutionary optimisation of Markov models of aero engines , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[63]  P. Frank.  Enhancement of Robustness in Observer-Based Fault Detection , 1991 .

[64]  Jie Chen,et al.  Robust detection of faulty actuators via unknown input observers , 1991 .

[65]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[66]  Abdul Raouf.  Maintenance Excellence: Optimizing Equipment Lifecycle Decision , 2004 .

[67]  G. Geiger,et al.  Monitoring of an Electrical Driven Pump Using Continuous-Time Parameter Estimation Methods , 1982 .

[68]  Rolf Isermann,et al.  Parameter adaptive control algorithms - A tutorial , 1982, Autom..

[69]  Andras Varga.  Integrated algorithm for solving H2-optimal fault detection and isolation problems , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[70]  Zhiwei Gao,et al.  Data-driven model reduction and fault diagnosis for an aero gas turbine engine , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[71]  Maria Laura Chiozza,et al.  FMEA: a model for reducing medical errors. , 2009, Clinica chimica acta; international journal of clinical chemistry.

[72]  N. Nichols,et al.  Robust pole assignment in linear state feedback , 1985 .

[73]  Keith J. Burnham,et al.  Robust filtering for a class of stochastic uncertain nonlinear time-delay systems via exponential state estimation , 2001, IEEE Trans. Signal Process..

[74]  Jie Chen,et al.  Design of unknown input observers and robust fault detection filters , 1996 .

[75]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[76]  Ke Zhang,et al.  Adaptive Observer-based Fast Fault Estimation , 2008 .

[77]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[78]  P. Dorato,et al.  Observing the states of systems with unmeasurable disturbances , 1975 .

[79]  Chong-Zhi Fang,et al.  Extended robust observation approach for failure isolation , 1989 .

[80]  Csr Times.  Robust sensor faults detection for induction motor using observer , 2012 .

[81]  Steven X. Ding,et al.  Fuzzy State/Disturbance Observer Design for T–S Fuzzy Systems With Application to Sensor Fault Estimation , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[82]  Paul M. Frank,et al.  Fault-diagnosis by disturbance decoupled nonlinear observers , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[83]  Minyue Fu,et al.  Robust 𝒽∞ filtering for continuous time varying uncertain systems with deterministic input signals , 1995, IEEE Trans. Signal Process..

[84]  Zhiwei Gao,et al.  Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosis , 2006, Syst. Control. Lett..

[85]  Peter J. Fleming,et al.  Application of system identification techniques to aircraft gas turbine engines , 2001 .

[86]  Da Silva,et al.  Induction Motor Fault Diagnostic and Monitoring Methods , 2006 .

[87]  Christopher Edwards,et al.  Sliding mode observers for robust detection and reconstruction of actuator and sensor faults , 2003 .

[88]  Peter Tavner,et al.  Condition Monitoring of Rotating Electrical Machines , 2008 .

[89]  Zhiwei Gao,et al.  High-gain observer-based parameter identification with application in a gas turbine engine , 2008 .

[90]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[91]  Friedrich Wilhelm Fuchs,et al.  Current Sensor Fault Detection, Isolation, and Reconfiguration for Doubly Fed Induction Generators , 2009, IEEE Transactions on Industrial Electronics.

[92]  G. Roppenecker.  On parametric state feedback design , 1986 .

[93]  Mehrdad Saif,et al.  Fault detection in a class of nonlinear systems via adaptive sliding observer , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[94]  Hong Wang,et al.  Actuator fault diagnosis: an adaptive observer-based technique , 1996, IEEE Trans. Autom. Control..

[95]  Halim Alwi,et al.  Robust fault reconstruction for linear parameter varying systems using sliding mode observers , 2014 .

[96]  R. J. Patton,et al.  An LMI approach to H - /H∞ fault detection observers , 2002 .

[97]  P. Frank,et al.  Fault detection via optimally robust detection filters , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[98]  N. Viswanadham,et al.  Robust Observer Design with Application to Fault Detection , 1988, 1988 American Control Conference.

[99]  Mehdi Gholizadeh,et al.  Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model , 2014, IEEE Transactions on Industrial Electronics.

[100]  Steven X. Ding,et al.  Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis , 1994, Autom..

[101]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[102]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[103]  James Lam,et al.  New approach to mixed H/sub 2//H/sub /spl infin// filtering for polytopic discrete-time systems , 2005, IEEE Transactions on Signal Processing.

[104]  Rik Pintelon,et al.  An Introduction to Identification , 2001 .

[105]  Gilbert Foo,et al.  Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives , 2013 .

[106]  S. Ding,et al.  Parameterization of linear observers and its application to observer design , 1994, IEEE Trans. Autom. Control..

[107]  H. Ting,et al.  Proportional-derivative unknown input observer design using descriptor system approach for non-minimum phase systems , 2011 .

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

[109]  S. Ding,et al.  Sensor fault reconstruction and sensor compensation for a class of nonlinear state-space systems via a descriptor system approach , 2007 .

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

[111]  Jie Chen,et al.  A robust disturbance decoupling approach to fault detection in process systems , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[112]  B. Singh,et al.  A review of stator fault monitoring techniques of induction motors , 2005, IEEE Transactions on Energy Conversion.

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

[114]  Sarah K. Spurgeon,et al.  Sliding Mode Observers for Fault Detection , 1997 .

[115]  Mohamed Ben-Daya,et al.  Handbook of maintenance management and engineering , 2009 .

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

[117]  Silvio Simani,et al.  Fault Diagnosis for Wind Turbine Systems , 2018 .

[118]  G. G. Leininger.  Model degradation effects on sensor failure detection , 1981 .

[119]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[120]  J. Gertler.  Fault detection and isolation using parity relations , 1997 .

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

[122]  B. Hahn,et al.  Reliability of Wind Turbines , 2007 .

[123]  James Lam,et al.  An LMI approach to design robust fault detection filter for uncertain LTI systems , 2003, Autom..

[124]  D. Ho,et al.  Proportional multiple-integral observer design for descriptor systems with measurement output disturbances , 2004 .

[125]  Guo-Ping Liu,et al.  Optimal residual design for fault diagnosis using multi-objective optimization and genetic algorithms , 1996, Int. J. Syst. Sci..

[126]  W. Ge,et al.  Detection of faulty components via robust observation , 1988 .

[127]  Vadim I. Utkin,et al.  Developing a fault tolerant power-train control system by integrating design of control and diagnostics , 2001 .

[128]  Yu Ding,et al.  Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process , 2010, IEEE Transactions on Power Systems.

[129]  Vaughn Nelson.  Wind Energy: Renewable Energy and the Environment , 2009 .

[130]  J.F. Frenzel,et al.  Genetic algorithms , 1993, IEEE Potentials.

[131]  Jakob Stoustrup,et al.  Active and passive fault-tolerant LPV control of wind turbines , 2010, Proceedings of the 2010 American Control Conference.

[132]  A. J. Laub,et al.  Algorithms and software for pole assignment and observers. Revision 1 , 1984 .

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

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

[135]  S. Ding,et al.  Fault Reconstruction for Lipschitz Nonlinear Descriptor Systems via Linear Matrix Inequality Approach , 2008 .

[136]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[137]  Paul M. Frank,et al.  Fault Detection and Isolation in Automatic Processes , 1991 .

[138]  Klaudia Frankfurter.  Eigenstructure Assignment For Control System Design , 2016 .

[139]  H. Marquez,et al.  Design of unknown input observers for Lipschitz nonlinear systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[140]  A. Hameed,et al.  Wind Turbines Control: Features and Trends , 2014 .

[141]  R. Nikoukhah,et al.  Fault detection in a mixed H2/H∞ setting , 2005, IEEE Trans. Autom. Control..

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

[143]  Seyedmojtaba Tabatabaeipour,et al.  Fault detection of a benchmark wind turbine using interval analysis , 2012, 2012 American Control Conference (ACC).

[144]  R. Clark.  The dedicated observer approach to instrument failure detection , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[145]  M. Boussak,et al.  Current sensors faults detection, isolation and control reconfiguration for PMSM drives , 2013, 2013 International Conference on Electrical Engineering and Software Applications.

[146]  Christopher A. Walford,et al.  Wind Turbine Reliability: Understanding and Minimizing Wind Turbine Operation and Maintenance Costs , 2006 .

[147]  Hamid Reza Karimi,et al.  Data-driven adaptive observer for fault diagnosis , 2012 .

[148]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[149]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[150]  S. Srinathkumar.  Robust eigenstructure assignment , 2011 .

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

[152]  Qinghua Zhang,et al.  An adaptive observer for sensor fault estimation in a class of uniformly observable non-linear systems , 2008, Int. J. Model. Identif. Control..

[153]  F.W. Fuchs,et al.  Current Sensor Fault Detection and Reconfiguration for a Doubly Fed Induction Generator , 2007, 2007 IEEE Power Electronics Specialists Conference.

[154]  Richard Vernon Beard,et al.  Failure accomodation in linear systems through self-reorganization. , 1971 .

[155]  Paul M. Frank,et al.  Sensor Fault Detection via Robust Observers , 1987 .