Transcript 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
Universidad de Valladolid
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Introduction: industrial process automation
Systems and faults:
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Diagnosis approaches
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Model – free methods
Model – based methods
Knowledge based methods
FDI: Fault detection and isolation
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What is a fault
Fault types
Fault detection
Fault isolation
Fault estimation
Evaluation FDI performance
Conclusions
Industrial Processes Automation 1
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Automatic
control
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Many advances in Control Engineering but:
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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
Industrial Processes Automation 2
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Malfunction causes:
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Design errors, implementation errors, human operator
errors, wear, aging, environmental aggressions
Fault
diagnosis
Fault Tolerant
Control
Safety Levels
 Detection
 Isolation
 Identification
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Predictive
Maintenance
Industrial Processes Automation 3
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Fault diagnosis:
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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:
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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
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Automatic
control
FDI
scheme
Fault Tolerant
Control
Safety Levels
 Detection
 Isolation
 Identification
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Predictive
Maintenance
Faults 1
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Some definitions:
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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
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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
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Where?
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• Leaks
• Overload
• Deviations
• Saturation
• Switch off
u
PLANT
ACTUATORS
• Bad calibrations
• Disconectings
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y
SENSORS
How ?
Abrupt
Evolutive
Fault
signal
Fault
signal
tf
tdet
Intermittent
Fault
signal
tf
Faults and 4
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Additive fault:
fault = f
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Multiplicative fault:
fault = a
FDI: Fault detection and isolation
-FDI
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methods :
model free methods (based on data)
knowledge based methods
model based methods
FDI methods 1
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Model free approaches: FDI methods based on data
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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
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Methods based on knowledge:
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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)
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FDI methods 3
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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
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Model based approaches:
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Compare actual system with a nominal model
system
Nominal system model
(Expected behavior)
Actual system behavior
COMPARISON
Detection
FDI methods 5
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Model based approaches: two main areas:
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FDI => from the control engineering point of view
DX => Artificial Intelligence point of view
From FDI:
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Models:
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Observers (Luenberger, unknown input etc.)
Kalman filters
parity equations
parameter estimation (Identification algorithms)
Extension to non linear systems (non-linear models)
FDI methods 6
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From DX:
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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
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Problem:
how to check the consistency
 How to find the collection of conflicts
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Qualitative and Semiqualitative models
FDI methods 7
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Models: are the output identical to the real
measurement?
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Construct the residuals: rt  y t  yˆ t
Test whether they are zero (true if logic) or not
Non zero => conflict (S is a NOGOOD)
Problem: rt  (ut , y t ,  t , v t , d)
noise
disturbances
Robust residual generation
or robust residual evaluation
uncertainties
FDI methods and 8
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FDI: Fault detection and Isolation
Decision theory:
Fault detection
Fault isolation
Fault estimation
Decision theory: fault detection
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Comparison of the residue with a threshold
Statistical decision: Hypotheses testing
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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
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Set based approach:
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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
Universidad de Valladolid
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FDI:
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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)
Universidad de Valladolid
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Incidence Matrix: dependence
between a fault (column) and
a residual (row) => 1
Coincidence between the
experimental and theoretical
incidence matrix
Bank of observers
= structured
residuals.
Decision theory: fault isolation (DX)
Universidad de Valladolid
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DX:
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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
Universidad de Valladolid
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The fault estimation consist of determining the
magnitude and evolution of the fault:
Choose a fault model:
ri (k)  Gij (q) * f j (k)
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Calculate the sensibility
function: Sij 
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Calculate the fault magnitude:
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dri
 Gij (q)
df j
r (k ) ri (k )
f j (k )  i

Sij
Gij (q)
FDI: Fault detection and Isolation
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Evaluation of FDI performance
Conclusions
Evaluation of FDI performance
Universidad de Valladolid
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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
Universidad de Valladolid
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FDI: a mature field
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Huge literature
SAFEPROCESS
European projects like MONET
Further research focuses on:
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New class of systems (e.g. Hybrid systems)
Applications
Fault tolerance issues
Bibliography
Universidad de Valladolid
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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)
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A.D. Pouliezos, G.D. Stavrakakis, (1994), Real time fault
monitoring of industrial processes, Kluwer Academic
Publishers
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J. Gertler (1998), Fault detection and diagnosis in
Engineering Systems, Marcel Dekker, New York
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J. Chen and R.J. Patton (1999), Robust model-based fault
diagnosis for dynamic systems, Kluwer Academic Publishers
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Etc…