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by

Luís Cláudio Oliveira-Lopes

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Outline The big picture

Fault in Industries

Fault detection

Methods and Examples

Diagnosis Isolation

Methods and Examples

Monitoring

Tolerant Control

Methods and Examples

Final Remarks

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Much attention?

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Much attention? More ...

Much more...

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Why do we need it?

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Fault

Detection

Tolerant Control

Process

Monitoring

Diagnosis/

Isolation

The Big Picture

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Abnormal Behavior Faults

Equipment Failures

Device Failures (sensors, actuator etc)

Control malfunction

Disturbances

Cyber attack

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Fault Tolerant Control

Predictive

Maintenance

Fault diagnosis

Safety Levels

Detection

Isolation

Identification

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Malfunction causes:

Design errors, implementation errors, human operator errors, wear, aging, environmental aggressions

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Fault diagnosis: Fault detection: Detecting 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

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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.

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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.

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Faults

ACTUATORS PLANT SENSORS u y

• Leaks

• Overload

• Deviations

• Bad calibrations

• Disconectings

• Saturation

• Switch off

Where?

Abrupt

tdet

Fault

signal

tf

How ?

Evolutive

tf

Fault

signal Fault

signal

Intermittent

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Model free approaches: FDI methods based on data

Only experimental data are exploited

Methods:

Alarms

Data analysis (PCA, PLS, CVA, FDA etc)

Pattern recognition

Spectrum analysis

Problems:

Need historical data in normal and faulty situations

Every faulty model is represented?

Generalisations capability?

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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)

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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 (SDG)

Symptoms trees.

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Fault from Cyber attack After detection, the line of defense should follow:

Containment

Remediation

Recovery/restauration

Prevention of reoccurrence

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After a cyber attack detection Actions require to know the consequences of the attack, its importance and its nature (internal/external)

Remediation may causes larger operational damages than the attack. If a valve was attacked itself (an embedded code of it) or the control system that control the valve was attacked play a great deal of importance in the response of any action one needs to perform.

If redundancy is available, one can use it while a careful study of the situation is performed.

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Fault Detection and Isolation (FDI) Data based methods

Use of historical data: PCA, ICA, ...

Need of normal operation data

Model based methods

Knowledge based method

Hybrid methods

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Model based approaches:

Compare actual system with a nominal model system

Actual system behavior Nominal system model (Expected behavior)

COMPARISON

DETECTION

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Model based approaches Two main areas:

FDI => from the control engineering point of view

DX => Artificial Intelligence point of view

From FDI:

Models:

Observers

Kalman filters

parity equations

parameter estimation (Identification algorithms)

Extension to nonlinear systems (nonlinear models)

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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

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Diagnosis/ Isolation Fault detection

Fault isolation

Fault estimation

Process behavior Diagnosis

Artificial Inteligence

Support Vector Machines

Fuzzy logic based classification

Neural Network based classification

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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

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Fault Isolation:

Fault isolability: provides the residuals with characteristic properties associated with one fault (one subset of faults)

Directional residues:

Structured residues:

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Evaluating FDI techniques False alarms: A fault detected when there is not

occurred a fault in the system

Missed detection: A fault is 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: influence of uncertainties, model mismatch, disturbances, noise ,...

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Confusion matrix

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AI Applied to Faut Detection and Isolation

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Support Vector Machine

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Example Fault Detection FDA

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Case Study

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Faults Fault 01 (CAf Perturbation): CAf is disturbed from

5.1 mol/L to 4.7 mol/L. from 2 h to 6 h;

Fault 02 (Faulty C B ): C B is 20% higher then correct measurement at the time instant of 4 h;

Fault 03 (Faulty T): T is 1% higher than the correct value at the time instant of 4 h, and

Fault 04 (Sticking valve): F is 30% lower than the one at steady state at the time instant of 4 h.

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SVM-R

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Example of Membership Function Used

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Fault

Detection

Tolerant Control

Process

Monitoring

Diagnosis/

Isolation

The Big Picture

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Monitoring Process Monitoring

Data based

Model based

Predefined metrics of performance

Propagation effects of faults

Alarm managment

Control tuning monitoring

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Study of the Propagation effect of Faults Souza & Oliveira-Lopes, 2014 Int. J. Reliability and Safety, Vol. 8, No. 1, 2014

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Faults Propagation in Batch Processes

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Sousa and Oliveira 2012

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Sousa and Oliveira-Lopes, 2014

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Faults Propagation in Continuous Systems

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Fault

Detection

Tolerant Control

Process

Monitoring

Diagnosis/

Isolation

The Big Picture

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Tolerant Control Control Reconfiguration

Tolerant Control – Robustness

Virtual Actuators

Control Allocation

Process Parking – Inherent Tolerant Control Current research

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Using PCA, DPCA etc

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Control Reconfiguration Reis & Oliveira-Lopes

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Control Reconfiguration

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Control Allocation Sousa and Oliveira-Lopes

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Schematic control structure using allocation

The process performance can be improved; It is able to separate and distribute the control task especially during faults; The control total effort is minimized;

Redundancy actuators

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Control Allocation based in Model Factorization

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Control Allocation:Two layers of control

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Toy Example

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Control Allocation And Opperability

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Virtual Actuator

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Final Remarks Classification Methods:

SVM (SVM AAA & SVM AAO). ANN (SNN, ANN FF and ANN WTA) and fuzzy logic;

Fault Detection and Diagnostics;

Fast and it can be implemented on-line;

Detection Errors;

Pre-processing (normalization, selection, labeling;

ANN (Slow Training) and Structure selection;

Necessary knowledge of fault pattern;

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Regression Based Methods SVM (SVR) and ANN (ANN-R);

Advantages:

Fast Detection;

Online Detection;

Smaller pre-processing requirments;

Disadvantages:

No isolation;

Slow training (ANN);

Selection of best structure (tuning);

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ACKNOWLEDGMENTS Lucas L. G. Reis (Aspentech, USA) Matheus H. Granzotto (UFVJM) Thiago V. Costa (UNIFEI) Davi Leonardo de Souza (UFTM) Nádia Guimarães Sousa (UFTM) Gustavo Almeida (UFMG) Flávio V. Silva (Unicamp) Panagiotis Christofides (UCLA) Christos Georgakis (Tufts) LOM Research Group at UFU