5th DAMADICS Workshop in Łagów Diagnosability and Sensor Placement. Application to DAMADICS Benchmark Ph. D. Student: Stefan Spanache Director: Dr. Teresa Escobet i Canal Co-Director: Dr. Louise Travé-Massuyès Departament d’Enginyeria de Sistemes, Automàtica i Informatica Industrial Universitat Politècnica de Catalunya
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Diagnosability and Sensor Placement. Application to DAMADICS Benchmark
Diagnosability and Sensor Placement. Application to DAMADICS Benchmark. Ph. D. Student:Stefan Spanache Director:Dr. Teresa Escobet i Canal Co-Director:Dr. Louise Travé-Massuyès Departament d’Enginyeria de Sistemes, Automàtica i Informatica Industrial - PowerPoint PPT Presentation
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5th DAMADICS Workshop in Łagów
Diagnosability and Sensor Placement. Application to DAMADICS Benchmark
Diagnosability and Sensor Placement. Application to DAMADICS Benchmark
Ph. D. Student: Stefan Spanache
Director: Dr. Teresa Escobet i Canal
Co-Director: Dr. Louise Travé-Massuyès
Departament d’Enginyeria de Sistemes, Automàtica i Informatica Industrial
Universitat Politècnica de Catalunya
5th DAMADICS Workshop in Łagów
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INDEX
0. Introduction
1. The objectives
2. Hypothetical Fault Signature Matrix
3. Minimal Additional Sensor Sets
4. Application example: DAMADICS Benchmark
5. Conclusions and future work
5th DAMADICS Workshop in Łagów
0. Introduction
INTRODUCTION 4
Model-based fault diagnosis methods
KNOWN INPUTS
PROCESS MODEL
DETECTION
ISOLATION
UNKNOWN INPUTS FAULTS
MEASURED STATE
ESTIMATED STATE
FAULT INDICATION
ISOLATED FAULT
INTRODUCTION 5
Analytical Redundancy Relations (ARRs)
5th DAMADICS Workshop in Łagów
1. The objectives
DIAGNOSABILITY AND SENSOR PLACEMENT
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The objectives
Main: design of an algorithm for
- set of additional sensors that can provide a maximum level of diagnosability
- cost optimisation method for these additional sensors
Given ( ,S,F) partially diagnosable, S is an Additional Sensor Set iff ( ,SS,F) is fully diagnosable.
Note: S is a set of hypothetical sensors.
S is a Minimal Additional Sensor Set (MASS) iff S' S, S' is not an Additional Sensor Set.
There are cases when this problem has no solution.
If S* is the set of all hypothetical sensors, then the fault signature matrix of
( ,SS*,F) is HFS.
Objective: finding all sets S with the properties:
i) dSS = dSS* and
ii) S' S, dSS = dSS*
Given ( ,S,F) partially diagnosable, S is an Additional Sensor Set iff ( ,SS,F) is fully diagnosable.
Note: S is a set of hypothetical sensors.
S is a Minimal Additional Sensor Set (MASS) iff S' S, S' is not an Additional Sensor Set.
There are cases when this problem has no solution.
If S* is the set of all hypothetical sensors, then the fault signature matrix of
( ,SS*,F) is HFS.
Objective: finding all sets S with the properties:
i) dSS = dSS* and
ii) S' S, dSS = dSS*
MINIMAL ADDITIONAL SENSOR SETS
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The procedure
HFS matrixHFS matrix
AFS matrixesAFS matrixes
Objective: finding all AFS matrixes with the rank equal to rank(HFS) and with minimal number of sensorsObjective: finding all AFS matrixes with the rank equal to rank(HFS) and with minimal number of sensors
5th DAMADICS Workshop in Łagów
4. Application example: DAMADICS Benchmark
Application example: DAMADICS Benchmark
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DAMADICS Benchmark (I)
The actuator consists in three main components:
control valve or hydraulic (H)
pneumatic servo-motor or mechanics (M)
positioner, which can also be decoupled in three components:
position controller (PC)
electro/pneumatic transducer (E/P)
displacement transducer (DT)
The actuator consists in three main components:
control valve or hydraulic (H)
pneumatic servo-motor or mechanics (M)
positioner, which can also be decoupled in three components:
The components that can be faulty: {M, P, H, DT, S(Ps), S(Fv), S(PV), S(dP), S(Pz)}
Considering only Sa = {S(Fv), S(PV), S(dP), S(Pz)}
The FS matrix:
The components that can be discriminated: {M,P,S(Pz)}, {H,S(Fv)}, DT, S(dP) and S(PV)
Discriminability level D = 5
The components that can be discriminated: {M,P,S(Pz)}, {H,S(Fv)}, DT, S(dP) and S(PV)
Discriminability level D = 5
Application example: DAMADICS Benchmark
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DAMADICS Benchmark (IV)
The HFS matrix after adding a sensor for PsThe HFS matrix after adding a sensor for Ps
The components that can be discriminated: M, {P,S(Pz)}, {H,S(Fv)}, DT, S(Ps), S(PV), S(PV)
Discriminability level D = 7
The components that can be discriminated: M, {P,S(Pz)}, {H,S(Fv)}, DT, S(Ps), S(PV), S(PV)
Discriminability level D = 7
Application example: DAMADICS Benchmark
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DAMADICS Benchmark (V)
The HFS matrix after adding a sensor for XThe HFS matrix after adding a sensor for X
The components that can be discriminated: {M, P,S(Pz)}, {H,S(Fv)}, DT, S(X), S(PV), S(dP)
Discriminability level D = 6
The components that can be discriminated: {M, P,S(Pz)}, {H,S(Fv)}, DT, S(X), S(PV), S(dP)
Discriminability level D = 6
5th DAMADICS Workshop in Łagów
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Conclusions and future work
Sensor availability provides a diagnosed system with Analytical Redundancy which, in turn, increases the Discriminability between the system components Given a required discriminability level Optimal
(discriminability/cost) instrumentation system can be found
Exhaustive search for best dS Optimisation of ds using Genetic Algorithms