ISIS project: Fault detection and diagnosis in process control
systems
Selected publications
Fault Diagnosis utilizing Structural Analysis, Mattias Krysander and
Mattias Nyberg, 2002
CCSSE, Norrköping, Sweden,
pdffile (206.7 kB),
abstract (786 byte) 
Improving fault isolability properties by structural analysis of
faulty behavior models: application to the DAMADICS benchmark problem,
E. Frisk, D. Düstegör, M. Krysander and V. Cocquempot , 2003
Proceedings of IFAC Safeprocess'03, Washington, USA,
pdffile (97.3 kB),
abstract (505 byte) 
A comparison of two methods for stochastic fault detection: the parity
space approach and principal component analysis, Anna Hagenblad, Fredrik
Gustafsson and Inger Klein, 2003, Proceedings of SYSID, pdffil (217.1 kB), psfil (969.6 kB). 
Design and Analysis of Diagnostic Systems Utilizing Structural Methods,
Mattias Krysander , 2003
Linköping University, LiUTEKLIC2003:37, Thesis No. 1038,
ISBN 917373733X, ISSN 02807971,
pdffile (1090.5 kB),
abstract (2.9 kB). 
Background
This project is carried out by the
Division of Automatic
Control and the
Division of Vehicular Systems in cooperation with
ABB
Automation Systems and
ABB Corporate Research.
Participant in the project from the Division of Automatic
Control has been
Anders Stenman, who is currently working at NIRA
Automotive AB. Since autumn 2000 Anna Hagenblad
works on this project together with Lennart Ljung, Inger Klein and Fredrik
Gustafsson, and in autumn 2001 Mattias Krysander
joined the project together with Mattias Nyberg
The aim is to study and develop methods
for detection and diagnosis in process control applications.
Statistical methods for fault detection and
diagnosis.
Anna Hagenblad
ABB Corporate Research is interested in developing a modelbased diagnostics
system for a highly automated pulp and paper plant. By using models of
different sections of the process and available sensors, we want to detect
and isolate faults, primarily in sensors and actuators.
This project focuses on fault detection and diagnosis in pulp and paper
processes. Typical characteristics of these systems are that they are large
systems with a large number of signals/sensors, and the physical models are of
limited accuracy.
We investigate
how to make a model of a system with a large number of signals, where
furthermore only a small part of the signal space contains data under normal
operations. PCA, principal component analysis is a promising method for this,
where singular value decomposition is used to find the relevant parts of the
signal space. The PCA model can then be used to compare measured process output
with model output, and compute a test statistic, which will differ from zero
when a fault has occured.
Once a fault is detected, the next step in the fault detection and diagnosis is
to find the faulty sensor. Using a probabilistic approach
we
can minimize the misclassification.
PCA has usually been employed for static systems, and for certain sampling
rates, the pulp and paper process can be regarded as such. It is however also
interesting to include dynamic information into the model, i.e., by including
delayed versions of the signals in the regressor. This is known as
dynamic PCA, dPCA, and closely related to subspace methods.
In [C4] the parity space approach to fault detection is compared
to PCA. It is assumed that there are additive faults on input
and output signals and stochastic unmeasurable disturbances.
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Structural analysis for design and analysis of diagnosis systems
Mattias Krysander
Introduction
Today many technical processes are complex and
highly integrated. When a process has failed, the complexity of the process
makes it hard for humans to troubleshoot it. To facilitate troubleshooting a
diagnostic system can supervise and alarm an operator when a fault is detected
and also identify one, or several faults, that may have caused the alarm. It is
a demanding and timeconsuming task to design a diagnostic system. Therefore
this project aims to develop algorithms and analysis methods that help and
automate the design of diagnostic systems.
In modelbased diagnosis a model of the
process is used to design a diagnostic system. This model describes the
different behaviours of the behavioural modes of the process, which are chosen
for the diagnosis task. Typical behavioural modes are the normally working mode
and faulty working modes. The behavior is assumed to be described using
differential algebraic equations.
In a diagnostic system a number of diagnostic
tests validate different subset of equation in the model, i.e. submodels, by
using observations of the process. Each test is based on a residual, derived
from the corresponding submodel. The residual is designed such that it is small
in the fault free case and large otherwise. If a submodel is
invalidated in a test, i.e. its residual is
significantly large, the conclusion is that the present
behavioural mode of the process belongs to a subset of considered behavioural
modes. With properly chosen tests, different subsets of tests are sensitive
to different subsets of faults. In this way identification, also called
isolation, of different faults can be achieved.
To invalidate a submodel, redundancy is needed. Overdetermined system of
equations have redundancy and are therefore suitable to test in a diagnostic
system. To
decide which equations each test should validate,
the structure of the model, i.e. which variables that are included in each
equation, is used. The structure is used to find particular set of equations
with more equations than unknowns called structurally overdetermined submodels.
Results and Developments
The results and developments are partitioned into the four parts
diagnosis framework,
developing algorithms for finding structurally overdetermined models,
predicting fault isolability properties using
structural analysis, and residual generation of
overdetermined submodels. In the licentiate thesis [T1] the three first
parts are thoroughly explained.
Diagnosis Framework
A new framework for model based diagnosis is presented in [C6] using ideas
from AI, FDI, and statistical hypothesis testing. The isolation mechanism also
studied in
[MSc2] is based on AI methods, and the main advantage is that multiple
faults are handled implicitly. Thus, no special care for isolation of multiple
faults is needed. It is assumed that a set of overdetermined submodels, that
for example is computed using structural methods, is tested. The methods for
residual generation, developed in the field of control theory (FDI), can within
the framework be fully utilized. Since the framework is also based upon
statistical hypothesis testing, it is suitable for problems including noise.
Developing Algorithms for Finding Structurally
Overdetermined Models
The models are as said before assumed to consist of a
set of nonlinear and linear differentialalgebraic equations. To find
tests by directly manipulating these equations is a
computationally complex task, especially for large and nonlinear
systems. To reduce the computational complexity of deriving tests, a twostep approach
is proposed in [C13] and [R1]. In the first step, the model is
analyzed structurally to find overdetermined
submodels. In the second, a residual for each of these submodels
is derived as described in [C6]. The benefit with this twostep
approach is that the submodels obtained are typically much smaller
than the whole model, and therefore the computational complexity of
deriving a residual from each submodel is substantially
lower compared to directly manipulating the whole model.
A structural algorithm that finds all
minimal structurally overdetermined submodels
in a model is given. It uses a new way of handling derivatives in structural
models and a comparison with other approches is studied
in [MSc3]. It is shown how the result of the
structural algorithm can be used to analyze the isolation capability of a diagnostic system
based on the computed submodels. Thereby it is possible minimize the number of
tests needed to obtain maximum isolability.
This algorithm is implemented in Matlab and
has been applied to a large nonlinear example, a part of a paper mill studied
in [MSc1]. In spite of the complexity of
this process, a small set of tests with high isolability is successfully
derived.
Predicting Fault
Isolability Properties Using Structural Analysis
As said in the previous section, structural methods can be used to compute which
submodels to test in order to obtain a diagnostic
system with high isolation capability. Since structural models are less
detailed than analytical models, structural models can be obtained
earlier in the design of a process. Since a structural model can be available
earlier in the development of the process, the design of a diagnostic system
can start earlier. This is advantageous because then it is possible to
consider the isolability aspects of for example sensor placement.
Furthermore a structural model is easier to
obtain than an analytical model, and structural analysis
is computationally less complex in many cases.
Efficient structural algorithms to compute isolabilty predictions are
developed in [C9]. In [MSc4], these algorithms are applied to an unmanned aerial
vehicle concept where isolability requirements have be derived from safety and
maintenance requirements [C8]. In spite of only using structural
models of the concept, the result of the analysis is that either a
specified set of optional sensors needs to be added, or some computed
isolability requirements cannot be fulfilled, or a new concept needs to be
developed. In [C5] it is shown how different levels of knowledge about faults
can be incorporated in a structural faultisolability analysis and how they
result in different isolability properties. The results are evaluated on the DAMADICS
valve benchmark problem. It is also shown how to determine which faults in the
benchmark that need further modelling to get desired isolability properties of
the diagnostic system.
Residual Generation of Minimal Overdetermined Models
As mentioned earlier any method
for residual generation, developed in the field of FDI, can be utilized for
minimal overdetermined models. However in [C7] a new method that uses the
minimallity property for finding
residual generators is developed. Two approaches are considered, one which is
based on the use of a dynamic numerical equation solver, and another which uses
a static numerical equation solver. The approaches are demonstrated on a
nonlinear pointmass satellite system.
In [C10] minimal overdetermined differential algebraic systems are considered.
By differentiating equations, a new set is formed, that is an overdetermined
static algebraic system if derivatives of unknown signals are considered as
separate independent variables. The task to derive analytical redundancy
relations is thereby reduced to an algebraic problem. It is desirable to
differentiate the equations as few times as possible and it is shown that there
exists a unique minimally differentiated overdetermined system.
Publications
Theses
[T1] 
Design and Analysis of Diagnostic Systems Utilizing Structural Methods,
Mattias Krysander , 2003
Linköping University, LiUTEKLIC2003:37, Thesis No. 1038,
ISBN 917373733X, ISSN 02807971,
pdffile (1090.5 kB),
abstract (2.9 kB). 
Conference Papers
[C1] 
Structural Analysis utilizing MSS Sets with Application to a Paper
Plant, Mattias Krysander and Mattias Nyberg, 2002, Proc. of the
Thirteenth International Workshop on Principles of Diagnosis (DX 2002),
Semmering, Austria,
pdffile (105.0 kB),
abstract (1020 byte) 
[C2] 
Structural Analysis for Fault Diagnosis of DAE Systems Utilizing MSS
Sets, Mattias Krysander and Mattias Nyberg, 2002, IFAC World Congress
, Barcelona, Spain,
abstract (881 byte) 
[C3] 
Fault Diagnosis utilizing Structural Analysis, Mattias Krysander and
Mattias Nyberg, 2002
CCSSE, Norrköping, Sweden,
pdffile (206.7 kB),
abstract (786 byte) 
[C4] 
A comparison of two methods for stochastic fault detection: the parity
space approach and principal component analysis, Anna Hagenblad, Fredrik
Gustafsson and Inger Klein, 2003, Proceedings of SYSID, pdffil (217.1 kB), psfil (969.6 kB). 
[C5] 
Improving fault isolability properties by structural analysis of
faulty behavior models: application to the DAMADICS benchmark problem,
E. Frisk, D. Düstegör, M. Krysander and V. Cocquempot , 2003
Proceedings of IFAC Safeprocess'03, Washington, USA,
pdffile (97.3 kB),
abstract (505 byte) 
[C6] 
Combining AI, FDI, and statistical hypothesistesting in a framework
for diagnosis, Mattias Nyberg and Mattias Krysander , 2003
Proceedings of IFAC Safeprocess'03, Washington, USA. 
[C7] 
Residual generators for DAE systems utilizing minimal subsets of
model equations, Jonas Biteus and Mattias Nyberg , 2003
Proceedings of IFAC Safeprocess'03, Washington, USA. 
[C8] 
A systematic inclusion of diagnosis performance in fault tree
analysis, Jan Åslund, Jonas Biteus, Erik Frisk, Mattias Krysander
and Lars Nielsen, submitted to IFAC World Congress, 2005, Prague,
Czech Republic. 
[C9] 
Predicting fault isolability properties using structural and
analytical information, Mattias Krysander and Mattias Nyberg,
submitted to IFAC World Congress, 2005, Prague, Czech Republic. 
[C10] 
Graph theoretical methods for finding analytical redundancy
relations in overdetermined differential algebraic systems, Mattias
Krysander and Jan Åslund, submitted to IMACS World Congress, 2005,
Paris, France. 
Technical Reports
[R1] 
Structural Analysis for Fault Diagnosis of DAE Systems Utilizing Graph
Theory and MSS Sets, Mattias Krysander and Mattias Nyberg, 2002,
LiTHR2410, Linköping University, SE581 83 Linköping, Sweden,
pdffile (493.4 kB),
abstract (1033 byte) 
[R2] 
A comparison of two methods for stochastic fault
detection: the parity space approach and principal component
analysis, Anna
Hagenblad,
Fredrik Gustafsson ,
Inger Klein
,, 2004,
LiTHR2636, Linköping University, SE581 83 Linköping, Sweden,
pdffil (217.1 kB), psfil (969.6 kB) 
Master Theses
[MSc1]

Diagnosis of Fluid Systems utilizing Gröbner Bases and Filtering of
Consistency Relations, Jonas Biteus, 2001
LiTHISYEX3237, Linköping University, SE581 83 Linköping,
pdffile (334.4 kB),
abstract (1372 byte) 
[MSc2]

Isolation of Multiplefaults with Generalized Faultmodes, Dan
Sune, 2002
LiTHISYEX33802002, Linköping University, SE581 83 Linköping,
pdffile (326.4 kB),
abstract (1226 byte) 
[MSc3]

A Comparative Study of Two Structural Methods for Fault Isolability
Analysis, Linda Rattfält, 2004
LiTHISYEX34622004, Linköpings Universitet, SE581 83 Linköping,
pdffile (326.4 kB),
abstract (1226 byte) 
[MSc4]

Diagnosis System Conceptual Design Utilizing Structural Methods 
Applied on a UAV's Fuel System, Tobias Axelsson, 2004
LiTHISYEX35522004, Linköpings Universitet, SE581 83 Linköping,
pdffile (326.4 kB),
abstract (1226 byte) 
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