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

Fault Detection and Isolation:an overview

María Jesús de la FuenteDpto. Ingeniería de Sistemas y

AutomáticaUniversidad de Valladolid

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

Automaticcontrol

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



Fault Tolerant Control

Predictive Maintenance

Faultdiagnosis

Safety Levels

Detection Isolation Identification

$

Malfunction causes: Design errors, implementation errors, human operator

errors, wear, aging, environmental aggressions



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


Industrial Processes Automation and 4

Automaticcontrol

Fault Tolerant Control

Predictive Maintenance

FDIscheme

Safety Levels

Detection Isolation Identification

$


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.


Faults 3

ACTUATORS PLANT SENSORSu y

• Leaks• Overload• Deviations

• Bad calibrations• Disconectings

• Saturation• Switch off

Where?

Abrupt

tdet

Fault signal

tf

How ?

Evolutive

tf

Fault signal

Fault signal

Intermittent


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.


FDI methods 4

Model based approaches: Compare actual system with a nominal model

system

Actual system behavior

Nominal system model

(Expected behavior)

COMPARISON

Detection


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


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

)d,v,,y,u(r ttttt

noise disturbances uncertainties

ttt yyr


FDI methods and 8

FDI: Fault detection and Isolation

Decision theory: Fault detectionFault isolationFault 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 and theoretical 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 one using 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:

)k(f*)q(G)k(r jiji

)q(Gdf

drS ij

j

iij

)q(G

)k(r

S

)k(r)k(f

ij

i

ij

ij

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 María Jesús de la Fuente Dpto. Ingeniería de Sistemas...

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