Fault Detection and Isolation: an overview María Jesús de la Fuente Dpto. Ingeniería de Sistemas y Automática Universidad de Valladolid

Outline Introduction: industrial process automation Systems and faults: What is a fault Fault types Diagnosis approaches Model – free methods Model – based methods Knowledge based methods FDI: Fault detection and isolation Fault detection Fault isolation Fault estimation Evaluation FDI performance Conclusions

Universidad de Valladolid Industrial Processes Automation 1 Automatic control Many advances in Control Engineering but: Systems do not render the services they were designed for Systems run out of control Energy and material waste, loss of production, damage the environment, loss of humans lives

Universidad de Valladolid Industrial Processes Automation 2 Fault Tolerant Control Predictive Maintenance Fault diagnosis Safety Levels  Detection  Isolation  Identification $ Malfunction causes: Design errors, implementation errors, human operator errors, wear, aging, environmental aggressions

Universidad de Valladolid Industrial Processes Automation 3 Fault diagnosis: Fault detection: Detect malfunctions in real time, as soon and as surely as possible Fault isolation: Find the root cause, by isolating the system component(s) whose operation mode is not nominal Fault identification: to estimate the size and type or nature of the fault. Fault Tolerance: Provide the system with the hardware architecture and software mechanisms which will allow, if possible to achieve a given objective not only in normal operation, but also in given fault situations

Universidad de Valladolid Industrial Processes Automation and 4 Automatic control Fault Tolerant Control Predictive Maintenance FDI scheme Safety Levels  Detection  Isolation  Identification $

Universidad de Valladolid Faults 1 Some definitions : Fault: an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable/usual/standard condition. Failure: a permanent interruption of a system’s ability to perform a required function under specific operating conditions. Disturbance: an unknown (and uncontrolled) input acting on the system which result in a departure from the current state. Symptom: a change of an observable quantity from normal behavior, i.e., an observable effect of a fault.

Universidad de Valladolid Faults 2 Reliability: ability of the system to perform a required function under stated conditions. Safety: ability of the system not to cause danger to persons or equipment or environment. Availability: probability that a system or equipment will operate satisfactorily at any point of time. Maintainability: concerns with the needs for repair and the ease with which repairs can be made.

Universidad de Valladolid Faults 3 ACTUATORS PLANT SENSORS uy   Leaks Overload Deviations Bad calibrations Disconectings Saturation Switch off Where? Abrupt t det Fault signal tftf How ? Evolutive tftf Fault signal Fault signal Intermittent

Universidad de Valladolid Faults and 4 Additive fault: fault = f Multiplicative fault: fault =  a

FDI: Fault detection and isolation - FDI methods : - model free methods (based on data) - knowledge based methods - model based methods

Universidad de Valladolid FDI methods 1 Model free approaches: FDI methods based on data Only experimental data are exploited Methods: Alarms Data analysis (PCA, SPL, etc) Pattern recognition Spectrum analysis Problems: Need historical data in normal and faulty situations Every fault model is represented? Generalisations capability?

Universidad de Valladolid FDI methods 2 Methods based on knowledge: Expert systems: diagnosis = heuristic process Expert codes his heuristic knowledge in rules: If set of symptoms THEN malfunction Advantage: consolidate approach Problems: Related to experience (knowledge acquisition is a complex task, device dependent) Related to classification methods (new faults, multiples faults) Related software: maintenance of the knowledge base (consistency)

Universidad de Valladolid FDI methods 3 Methods based on soft-computing: combination of data and heuristic knowledge Neural networks Fuzzy logic Genetic algorithms Combination between them Causal analysis techniques: are based on the causal modeling of fault-symptom relationships: Signed direct graphs Symptoms trees.

Universidad de Valladolid FDI methods 4 Model based approaches: Compare actual system with a nominal model system Actual system behavior Nominal system model (Expected behavior) COMPARISON Detection

Universidad de Valladolid FDI methods 5 Model based approaches: two main areas: FDI => from the control engineering point of view DX => Artificial Intelligence point of view From FDI: Models: Observers (Luenberger, unknown input etc.) Kalman filters parity equations parameter estimation (Identification algorithms) Extension to non linear systems (non-linear models)

Universidad de Valladolid FDI methods 6 From DX: Based on consistency: OBS: (set of observations) SD: system description: the set of constraints COMP: set of components of the system Fault detection: SD  OBS  {OK(X)  X  COMPS} is not consistent NG: (conflict or NOGOOD): if NG  COMPS and SD  OBS  {OK(X)  X  COMPS} is not consistent Problem: how to check the consistency How to find the collection of conflicts Qualitative and Semiqualitative models

Universidad de Valladolid FDI methods 7 Models: are the output identical to the real measurement? Construct the residuals: Test whether they are zero (true if logic) or not Non zero => conflict (S is a NOGOOD) Problem: Robust residual generation or robust residual evaluation noisedisturbancesuncertainties

Universidad de Valladolid FDI methods and 8

FDI: Fault detection and Isolation Decision theory: Fault detection Fault isolation Fault estimation

Universidad de Valladolid Decision theory: fault detection Comparison of the residue with a threshold Statistical decision: Hypotheses testing H 0 : the data observer on [t 0, t f ] may have been produced by the healthy system H 1 : the data observer on [t 0, t f ] cannot been produced by the healthy system, i.e., there exist a fault

Universidad de Valladolid Decision theory: fault detection Set based approach: construct the set of trajectories which are possible taking into account uncertainties and unknown inputs Under /over-bound approximations: Fault detectability.

Universidad de Valladolid Decision theory: fault isolation FDI: Fault isolability: provide the residuals with characteristic properties associated with one fault (one subset of faults) Directional residues: Structured residues:

Universidad de Valladolid Decision theory: fault isolation (FDI) Bank of observers = structured residuals. Incidence Matrix: dependence between a fault (column) and a residual (row) => 1 Coincidence between the experimental and theoretical incidence matrix

Universidad de Valladolid Decision theory: fault isolation (DX) DX: Detect the conflicts, i.e., find all NOGOODS. To enunciate all the faulty systems components To compute the minimal hitting set: from all candidates to choose the best one using the consistency reasoning.

Universidad de Valladolid Decision theory: fault estimation The fault estimation consist of determining the magnitude and evolution of the fault: Choose a fault model: Calculate the sensibility function: Calculate the fault magnitude:

FDI: Fault detection and Isolation - Evaluation of FDI performance - Conclusions

Universidad de Valladolid Evaluation of FDI performance False alarms: A fault detected when there is not occurred a fault in the system Missed detection: A fault do not detected Detection time: (delay in the detection) Isolation errors: distinguish a particular fault from others Sensibility: the size of fault to be detected Robustness: (in terms of uncertainties, models mismatch, disturbances, noise,...)

Universidad de Valladolid Conclusions FDI: a mature field Huge literature SAFEPROCESS European projects like MONET Further research focuses on: New class of systems (e.g. Hybrid systems) Applications Fault tolerance issues

Universidad de Valladolid Bibliography R.J. Patton, P. Frank and R. Clark (1989), Fault Diagnosis in Dynamic Systems. Theory and applications. Control Engineering Series, Prentice Hall ( A new edition in 2000) A.D. Pouliezos, G.D. Stavrakakis, (1994), Real time fault monitoring of industrial processes, Kluwer Academic Publishers J. Gertler (1998), Fault detection and diagnosis in Engineering Systems, Marcel Dekker, New York J. Chen and R.J. Patton (1999), Robust model-based fault diagnosis for dynamic systems, Kluwer Academic Publishers Etc…