Fault Detection and Isolation: an overview

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

Industrial Processes Automation 1 • 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 Automatic control

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

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

Safety Levels • Detection • Isolation • Identification FDI scheme $ Industrial Processes Automation and 4 Automatic control Fault Tolerant Control Predictive Maintenance

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.

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.

Leaks • Overload • Deviations Where? • Saturation • Switch off • Bad calibrations • Disconectings   u y PLANT ACTUATORS SENSORS How ? Intermittent Abrupt Evolutive Fault signal Fault signal Fault signal tf tdet tf Faults 3

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

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?

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)

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.

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

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)

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

noise disturbances uncertainties 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

FDI methods and 8

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

Decision theory: fault detection • Comparison of the residue with a threshold • Statistical decision: Hypotheses testing • H0: the data observer on [t0, tf] may have been produced by the healthy system • H1: the data observer on [t0, tf] cannot been produced by the healthy system, i.e., there exist a fault

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.

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:

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 andtheoretical incidence matrix

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 oneusing the consistency reasoning.

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

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 ,...)

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

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…

Fault Detection and Isolation: an overview