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General information
Name: Silvio Simani
Gender: Male
Place and Date of Birth Ferrara
(Italy), April 21, 1971
University Address Dipartimento
di Ingegneria, Università di Ferrara
Via Saragat,1 44100 Ferrara, Italy
Phone: +39 0532 191 2034
Fax: +39 0532 97 4870
Home Address Via Bologna, 1/E
44100 Ferrara, Italy
E-Mail: ssimani@ing.unife.it
Webpage: http://www.ing.unife.it/simani
Education
1990-1996 Department of
Engineering, University of Ferrara (Italy): “Laurea” degree (cum laude) in
Electronic Engineering. (June 16, 1996).
1996-1999 Department of
Engineering Science, University of Modena and Reggio Emilia (Italy): Ph.D. in
Automatic Control. (February 25, 2000).
In more details, Dr. Silvio Simani graduated in Electronic Engineering
in 1996 at the University of Ferrara, Italy. His final dissertation, whose
title is "Visual
motion and structure estimation", developed at the VIS.I.T. Laboratory of the CINECA of Casalecchio (BO) (coordinated by
Prof. Sergio Beghelli and Dr. Maurizio Forte), consisted of the design,
construction, modelling and validation of a software program in order to estimate
the 3D structure of an object observed by means of a mobile camera. The work
consisted of three main parts.
These three parts gave rise to an experimental validation of the model
by comparison of simulated and measured data. The final procedure was applied
to a real problem: the reconstruction of the 3D structure of the skull of a
prehistoric man from raw images taken by a mobile camera. The skull of the Altamura Man in
the Altamura grotto, Italy.
Dr.
Simani’s study curriculum has been focused mainly on Systems and Control
Theory. In particular, in 1999 Dr. Simani finished his PhD study course in
System and Control Engineering at the University of Ferrara, Department of
Engineering (Italy). During the PhD years, Dr. Simani was mainly involved in
the following topics:
In the years from 1997 to 2000, Dr. Simani attended the following
postgraduate courses:
Dr. Simani is currently involved in cooperative projects regarding the
fault diagnosis of dynamic processes, and in particular regarding gas turbines,
together with ATSOM Ltd. (UK), The University of Hull (UK) and The University of Strathclyde (UK). Because
of these projects, from January 1999, Dr. Simani has been working and
collaborating with Prof. Ron
J. Patton in the Control Systems Engineering Laboratory of Faculty of
Mathematics & Engineering at The University of Hull (UK).
In
February 25th, 2000, Dr. Simani was awarded the Ph.D. in
“Information Science: Automatic Control” at the Department of Engineering of
the University of Ferrara, Italy. A the end of December, 2000, he obtained a
Postdoctoral Position on Quantitative and Soft Computing Methods for Fault
Diagnosis in the "EC RTN
DAMADICS" at the Faculty of Mathematics
& Engineering at The University of Hull (UK).
He
is currently collaborating with Department of Engineering at the University of
Ferrara and working on several national and international projects regarding
the field of fault detection and diagnosis in dynamic systems, dynamic system
identification, fuzzy logic, neural networks and nonlinear system
identification for fault diagnosis (hybrid systems).
After
few years as part-time assistant lecturer (1998 - 2001), from February 2002 Dr.
Simani is Assistant Professor at the Department of Engineering of the
University of Ferrara. His research interests include fault diagnosis of
dynamic processes, system modelling and identification, and the interaction
issues between identification and fault diagnosis.
professional
Experience
1996-1997.
Research Fellow in Science and Supercomputing at CINECA.
CINECA Computing Centre. Casalecchio di Reno, Bologna (Italy).
1998. Part Time Assistant Lecturer.
Department of Engineering, University of Ferrara (Italy)
1999. Part Time Assistant Lecturer.
Department of Engineering, University of Ferrara (Italy)
2000. Part Time Assistant Lecturer.
Department of Engineering, University of Ferrara (Italy)
2000.
Electronics &
Mechanics Collaborative Project.
Department of Engineering, University of Ferrara (form June to September, 2000)
2000. Post
Doc Fellow. Research Contract at the Department of Mathematics &
Engineering, The University of Hull (UK)
2001 Part Time Lecturer.
Department of Engineering, University of Ferrara (Italy)
2002 Part Time Lecturer.
Department of Engineering, University of Ferrara (Italy)
2002 Full time
assistant professor & lecturer at the Department of Engineering of
University of Ferrara (Italy) (since February 2002)
In more details, Dr. Simani was involved in the Shape from Motion
Project: 3D Modelling by
Analogic Video Input Data for the Reconstruction of Archaeological Sites,
developed in the VIS.I.T laboratory
of the Supercomputing Center CINECA
(Casalecchio, BO, Italy) in 1997, sponsored by the AIACE Association.
He contributed to the International Summer School "Fuzzy Logic Control:
Advances in Methodology", held in Ferrara (Italy) on July 16 - 20,
1998.
From the beginning of 1997 he has been
involved in research programs within the Automatic Controls & Robotics
research group of the Dipartimento di Ingegneria, Università di Ferrara (Prof.
Prof. Sergio Beghelli
and Prof. Cesare Fantuzzi). His
main research interests are in the design and development of automatic fault
detection and isolation procedures regarding linear dynamic systems (industrial
processes and power plants), modelling and identification of linear and
non-linear dynamic models, fuzzy modelling and control of linear and non-linear
algebraic and dynamic systems.
From 1999, Dr. Simani has been
collaborating on cooperative works and projects with ATSOM Ltd. (UK), The University of Hull (UK) and The University of Strathclyde (UK). The
main topics of the projects consist in actuator, component and sensor FDI
concerning a model of a single-shaft industrial gas turbine. Because of such a
project, Dr. Simani is currently working with Prof. Ron J. Patton in the
Control Systems Engineering Laboratory of Faculty of Mathematics & Engineering
at The University of Hull (UK).
In August 30, 1999 Dr. Simani attended
the Tutorial Workshop "Knowledge-Based
Fault Detection and Diagnosis Systems" within the ECC’99, held in
Karlsruhe (Germany) and organised by EUCA and IFAC. In September 8-11, 1999 Dr.
Simani attended the "Iterative
Identification and Control Design" International School (An ESF
course), held in Valencia (Spain) and organised by DISA and CEA-IFAC.
From 1999 to 2002, Dr. Simani worked as
Part Time Assistant Lecturer at Department of Engineering of the University of
Ferrara (Italy). From June to September 2000, he joined an Electronics &
Mechanics Collaborative Project at Department of Engineering of the University
of Ferrara.
At the end of 2000, Dr. Simani obtained a
Postdoctoral Position on Quantitative and Soft Computing Methods for Fault
Diagnosis in the "EC RTN
DAMADICS" at the Faculty of
Mathematics & Engineering at The University of Hull (UK).
From March to June, 2001 Dr. Simani
worked as Part Time Lecturer at the "CFP
CESTA". CENTRO FORMAZIONE PROFESSIONALE CESTA - COPPARO (FERRARA,
Italy).
Starting from 2002, Dr. Simani is
currently collaborating with Department of Engineering at the University of
Ferrara as Assistant Professor and working on several national and
international projects regarding the field of fault detection and diagnosis in
dynamic systems, dynamic system identification, fuzzy logic, neural networks
and nonlinear system identification for fault diagnosis (hybrid systems).
Dr. Simani has also the following skills
in the computer science field:
ACADEMIC Activity
Courses & Lessons (in Italian &
English)
§
“Systems &
Automatics Laboratory”: 70 hours course module for the 3rd year
Automatic Control, Informatics, Telecommunications and Electronics Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 1998/1999.
§
“Systems &
Automatics Laboratory”: 70 hours course module for the 3rd year
Automatic Control, Informatics, Telecommunications and Electronics Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 1999/2000.
§
“Automatic
Fault Diagnosis - Fault Diagnosis of Dynamic Systems Using Model-Based and
Filtering Approaches”: 10 hours minicourse for the 5th year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2000/2001.
§
“Automatics
Laboratory: Part I”: 50 hours course Module for the 3rd year
Automatic Control, Informatics, Telecommunications and Electronics Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 2000/2001.
§
“Automatics
Laboratory: Part I”: 50 hours course module for the 3rd year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2001/2002.
§
“Systems &
Automatics Laboratory”: 50 hours course module for the 4th year
Automatic Control, Informatics, Telecommunications and Electronics Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 2001/2002.
§
“Automatics
Laboratory: Part I”: 50 hours course module for the 3rd year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2002/2003.
§
“Automatics
Laboratory: Part I”: 50 hours course module for the 3rd year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2003/2004.
§
“Dynamic
System Identification & Data Analysis”. 56 hours course module for the
3rd year and 5th year Automatic Control Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 2003/2004.
§
“Automation
Technology Laboratory - Neural Networks for Identification, Prediction and
Control”. 10 hours course submodule for the 5th year Automatic
Control Engineering Students. Department of Engineering, University of Ferrara.
Academic Year Calendar 2003/2004.
§
“Dynamic
System Identification & Data Analysis”. 56 hours course module for the
3rd year and 5th year Automatic Control Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 2004/2005.
§
“Residual
Generator Computation via Polynomial Approach for FDI”. Minicourse for the
European Ph.D. in Information Technology organised and supported by the ARCES
Centre (Advanced Research Centre on Electronic Systems for Information and
Communication Technologies "Ercole De Castro") of the University of
Bologna. (Italy) Academic Year Calendar 2004/2005 (in English). ARCES Centre.
§
“Automatics
Laboratory: Part I”: 50 hours course module for the 2nd year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2004/2005.
§
“Automation
Technology Laboratory - Neural Networks for Identification, Prediction and
Control”. 10 hours course submodule for the 5th year Automatic
Control Engineering Students. Department of Engineering, University of Ferrara.
Academic Year Calendar 2004/2005.
§
“Dynamic
System Identification & Data Analysis”. 56 hours course module for the
3rd year and 5th year Automatic Control Engineering
Students. Department of Engineering, University of Ferrara. Academic Year
Calendar 2005/2006.
§
“Automatics
Laboratory: Part I”: 50 hours course module for the 2nd year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2005/2006.
§
“Automation Technology
Laboratory - Neural Networks for Identification, Prediction and Control”.
10 hours course submodule for the 5th year Automatic Control
Engineering Students. Department of Engineering, University of Ferrara.
Academic Year Calendar 2005/2006.
§
“Kalman
Filtering (KF): Theory and Applications”. Lecture for the Summer School on
Advanced Technologies for Neuro Motor assessment and rehabilitation. Academic
Year Calendar 2005/2006 (in English). Regional STARTER Project: Strategic
Network for Assistive and Rehabilitation Technology in Emilia-Romagna (Italy).
§
“Automatic
Fault Diagnosis - Fault Diagnosis of Dynamic Systems Using Model-Based and
Filtering Approaches”: 10 hours minicourse for the 5th year
Automatic Control Engineering Students. Department of Engineering, University
of Ferrara. Academic Year Calendar 2006/2007.
Other
From 2000-2002 Dr. Simani was organiser
of several invited sessions at the IEEE International Conferences – CDC’s,
SAFEPROCESS’s Symposium, and member of the Technical Committee at the IEEE
ACC’2004 for the IEEE CDC’04 in 2004.
Dr.
Simani is author of more than 80-refereed journal and conference papers and one
book on these topics. He has served as reviewer for several international
journals and conferences, as listed below:
§
Automatica
§
IEEE Transactions
on Automatic Control
§
International
Journal of Control
§
IEEE Transactions
on Control Systems Technology
§
Control
Engineering Practice
§
International
Journal of Systems Science - IJSS
§
Transactions of
the Institute of Measurement and Control
§
IEEE transactions
on Industrial Electronics
§
IEEE Transactions
on Fuzzy Systems
§
European Journal
of Control
§
International
Journal of Intelligent Systems Technologies and Applications
§
IEEE Transactions
on Signal Processing
§
International
Journal of Adaptive Control and Signal Processing
§
International
Journal of Nonlinear and Robust Control
§
IEEE Signal
Processing Letters
§
IEEE Transactions
on Signal Processing
§
IEEE Transactions
on Speech and Audio Processing
§
IEEE Transactions
on Image Process
§
IEEE Transactions
on Industrial Informatics
§
International
Journal on Mechatronics
§
Fuzzy Sets and
Systems Journal
§
Journal of Neural
Processing Letters
§
Journal of Latin
American Applied Research - LAAR
§
Transactions of
the Society for Modelling and Simulation International
§
Journal of
Aerospace Computing, Information, and Communication
§
IFAC SAFEPROCESS
Symposium
§
IEEE CDC
Conference – Conference on Decision and Control
§
IEEE Control
Conference
§
IEEE ACC
Conference – American Control Conference
§
IEEE CCA
Conference – Conference on Control and Applications
§
ECC - European
Control Conference
§
IEEE MMAR -
International Conference on Methods and Models in Automation and Control
Professional Membership
1998 Member of the IEEE
Institute of Electrical &
Electronic Engineers
2000 Member of the IFAC
Technical Committee SAFEPROCESS
2000 Post Doc Research
Contract at the University of Hull, UK.
2002 Assistant Professor at
the Department of Engineering of the University of Ferrara Italy (permanent
position).
Research Interests
1) Identification and model-based fault
diagnosis of industrial processes.
2) Linear identification of dynamic processes
from noisy data.
3) Fuzzy modelling, identification and control
of dynamic systems.
4) Neural Networks for fault identification.
5) Hybrid model identification from noisy data
In particular, for the Fault Diagnosis
topic:
1)
Identification and model-based fault diagnosis of real and simulated industrial
processes.
2) Actuator,
component and sensor fault diagnosis using identification techniques.
3) Robust
model-based methods for residual generation.
In more details, regarding Fault Detection and Diagnosis in Dynamic
Systems, Dr. Simani’s interests are in the field of fault diagnosis in
dynamic systems and in particular, since 1997, he is carrying out researches
into the development of methods for rapid detection and diagnosis of faults in
input-output control sensors of industrial processes. The interest has been in
the use of analytical redundancy methods based on information implicit in
functional or analytical relationships, which always exist between a number of
measurements taken from a process (e.g. industrial plant). The research has
exploited methods, using robust state observers, which have provided a
capability for reliable detection of faults in the presence of typical plant
parameter variations. These methods enable faults to be isolated, in addition
to being detected. Furthermore, the reliable detection and isolation of
multiple faults has also been experimented. He has worked on application
studies ranging from industrial gas turbines and power plants (e.g. Pont
sur Sambre). The research is being driven by industrial application studies,
although a particular interest has been the development of a unifying theory
for model-based diagnosis algorithms. Dr. Simani is trying to apply the
experimented methods within the context of robustness to uncertainty.
Concerning the
Linear & nonlinear system identification topics, the project is
concerned with problems in identification of linear and nonlinear systems.
Special emphasis is given to the case of multi-input single-output (MISO) and
multi-input multi-output (MIMO) systems. The latter case shows even in the
linear case considerable more complexity when compared to the single-input
single-output (SISO) case. It should be noted that identification of linear
systems is a highly nonlinear task; the results obtained for the linear case
also have a pivotal character for identification of nonlinear systems. The
problems considered range from structure theory (realization and
parameterisation) to estimation algorithms (including their evaluation). The
main topics of research are:
·
Parameterisation: The property of parameterisations, for linear
systems, in particular of the so-called balanced realizations, is investigated.
·
Subspace-methods: For 'large' MIMO systems the standard
identification procedures, like Maximum likelihood methods and Prediction error
methods, have high numerical complexity. The so-called subspace methods (SSM)
are numerically faster, however their statistical properties have not been
fully investigated yet.
·
Algorithms for
dynamic errors-in-variables models: here algorithms for the estimation of the set of all observationally
equivalent systems shall be developed. In addition a test, whether this
equivalence class contains a causal system, shall be constructed. The
statistical properties of these algorithms are evaluated.
·
Regularization
and complexity: A second
approach, to overcome the numerical problems in identification of MIMO systems,
might be the use regularization methods. The statistical properties of such
methods are evaluated. These regularization methods seem to be promising also
for the identification of nonlinear models (e.g. neural networks). The results
for the linear case are generalized to certain classes of nonlinear systems.
The Fuzzy logic in control and identification item focuses on 2
points:
·
Fuzzy logic in
control: This activity is
aimed to investigate the application of the fuzzy logic paradigm for the
control and identification of dynamic system. In particular, fuzzy logic in
control has been successfully used to capture heuristic control laws obtained
from human experience or engineering practice in automated algorithm. These
control laws are defined by means of linguistic rule, for example "if the
pressure is high, then decrease the pump power". The heuristic approach in
the controller design can be appealing for its simplicity, but formal design
method can be mandatory in some cases. For this reason a great endeavour is
carrying out by several researcher in defining formal design procedures.
·
Fuzzy model
identification: In literature
a general approach to nonlinear structure modelling does not exist and then
fuzzy models are interesting because they can approximate a large class of
nonlinear functions. The maim problem consists in finding the parameters of the
fuzzy model from data affected by noise. A well-established procedure, the
Frisch Scheme, for linear identification in stochastic environment has been
modified and exploited to be applied to fuzzy model identification. The
extension of the implemented technique for the identification of
multidimensional piece-wise linear causal models and piece-wise linear fuzzy
models allows to identify nonlinear dynamic models.
The Neural Network research activity regards 2 aspects:
·
Static neural
networks in fault
diagnosis: In recent years, neural networks have been exploited
successfully in pattern recognition as well as function approximation theory
and they have been proposed as a possible technique for fault diagnosis,
too. Neural networks can handle nonlinear behaviour and partially known
process because they learn the diagnostic requirements by means of the
information of the training data. They are noise tolerant and their ability to
generalize the knowledge as well as to adapt during use are extremely
interesting properties. The major motivation of this research is the use of
artificial static neural networks, capable of approximating a large class of
functions, for fault diagnosis of an industrial plant, too. The ultimate
goal is to develop a general method, which can be applied to a broad spectrum
of processes. In particular, in the detection and diagnosis problem, neural
networks are exploited to estimate the relationship between symptoms and
faults. In such a way, residuals generated by means of state estimation
techniques are independent of the dynamic characteristics of the plant and
dependent only on sensors faults. Therefore, the neural network evaluates
static patterns of residuals, which are uniquely related to particular fault
conditions independently from the plant dynamics.
·
Dynamic neural
networks in system
identification: This research concerns model identification by linear and nonlinear
dynamic neural networks. Linear networks may be used to model real systems. If
the real system is linear or near linear then the linear network can act as a
zero, or low, error model. In the real system is nonlinear, linear network
models the system with minimum sum-squared error. Nonlinear networks can be
used to identify a nonlinear system. Two networks are commonly used: Elman
and Hopfield networks. Elman networks are two-layer backpropagation
networks, with the addition of a feedback connection from the output of the
hidden layer to its input. This feedback path allows Elman networks to learn to
recognize and generate temporal patterns. The Hopfield network is used to store
one or more stable target vectors. These stable vectors can be viewed as
memories that the network recalls when provided with similar vector, which acts
as a cue to the network memory.
Finally, the Identification of Nonlinear Dynamic Systems is
solved via a multiple model approach. In particular:
·
The Fault
diagnosis of nonlinear dynamic systems: Fault Diagnosis (FD) for industrial processes requires reliable models
for effective malfunctioning detection. As linear models are seldom effective
in describing complex industrial processes, more complicated non-linear models
should be used for this purpose. The construction of structured non-linear
model from input-output data is currently under investigation in several
institutes and laboratories all over the world but is far to be fully
established, especially when we consider that in industrial environment the
acquired data are always affected by noise. A further problem is to consider
that the identification procedure should be coupled to a fault diagnosis
algorithm. In order to establish a methodology applicable in a wide class of
industrial plants, we can observe that in many cases the processes can be
described using simple model having a local validity around an operating
condition. Therefore, instead of exploiting complicated non-linear models
obtained by modelling techniques, it is also possible to describe the plant by
a collection of affine models. Each submodel approximates the system locally
around a working condition and a selection procedure determines which
particular submodel has to be used. Such a multiple model structure is called multiple
model approach. At each operating point, the behaviour of the multiple
model is described by a local affine dynamic model. Several researchers
currently explore this approach; among them we can cite the works by Billings
and Leonaritis, Takagi and Sugeno, Branicky and Benveniste. However little
attention has been paid to the problem of noise affecting plant measurements,
which can significantly decrease the suitability of the model in the context of
FD, in particular producing false alarms. As in practical condition the
measured data are always affected by noise, this problem is really important to
be carefully considered. Some preliminary study have been already carried on
this stream, particularly in the context of linear models, by Kalman and
Beghelli, and recently for non linear models, by Beghelli, Fantuzzi, Rovatti
and Simani.
·
Multiple model
approach: The construction of
the multiple model from only one set of global input-output noisy measurements
is a non-trivial problem since the model structure, a switching function and
the local model parameters have to be identified. The technique we aim to
develop concerns the estimate of the operating point regions, the
identification of the structure and parameters of the piecewise affine system
based on local linear models from input-output data affected by noise. A
non-linear dynamic process is, in fact, described as a composition of several
local submodels selected according to the process operating conditions. This
project addresses a method for the identification and the optimal selection of
the local submodels from a sequence of noisy measurements acquired from the
process. In particular, in this project, a novel non-linear identification
technique is combined with the model-based method to formulate a fault
detection and isolation (FDI) tool exploiting the multiple model approach for
residual generation. The model for non-linear dynamic systems is described by a
number of local linear models. Each submodel approximates the system locally
around an operating point and a selection procedure determines which particular
submodel has to be used. Under such a new identification method, a number of
local linear models are designed and the estimate of outputs is given by a
combination of local outputs. The diagnostic signal (residual) is the
difference between the estimated and actual system output. The key idea of model-based
approaches for FD is, in fact, the generation of signals, termed residuals,
obtained by using observers, parameter estimation or parity equations designed
on the basis of mathematical models of the monitored system. The success of the
model based method is heavily dependent on the quality of models. Instead of
exploiting complicated non-linear models obtained by modelling techniques, the
problem is overcome describing the plant by a collection of local linear models
obtained by the non-linear identification method presented above. The
contribution of this research is two fold. First, it is shown how to integrate
the well-established Frisch scheme method for the identification of affine
algebraic systems within a general procedure for non-linear dynamic system.
Second, some interesting properties of such a Scheme can enhance the solution
of the optimisation problem as well as of the continuity constraint fulfilment.
Local Research Activity
Dr. Simani’s research interests and activities regard the detection,
isolation and identification (FDI) of faults regarding actuator, component and
sensors of dynamic systems. Moreover, since a FDI model based-approach is
exploited, an accurate model of the process under investigation is required.
Therefore, another study area of mine addresses the modelling and
identification of non-linear systems, fuzzy modelling and identification and
control of linear and non-linear algebraic and dynamic models.
From the beginning of October to the end of December, 2000, Dr. Simani
was involved in research program/project within the Control & Intelligent
Systems Engineering research group of the Department of Mathematics &
Engineering at the University of Hull (with Prof. Ron J. Patton). His main
research activity consisted in the design and development of automatic fault
detection and isolation procedures regarding non-linear dynamic systems (industrial
processes and power plants, such as a model of a Continuous Stirred Tank
reactor System and an a real Sugar Factory process), modelling and
identification of linear and non-linear dynamic models, fuzzy modelling and
control of linear and non-linear algebraic and dynamic systems.
Dr. Simani is currently collaborating with the Department of Engineering
at the University of Ferrara (Italy) within the Automatic Controls &
Robotics Group (with Prof. Sergio Beghelli and Prof. Cesare Fantuzzi) as well
as the Department of Mathematics and Engineering at the University of Hull (UK)
within Control & Intelligent Systems Engineering Research Group (Prof. Ron
J. Patton) and working on other national and international projects regarding
the field of fault detection and diagnosis in dynamic systems, dynamic system
identification, fuzzy logic, neural networks and non-linear system
identification for fault diagnosis (hybrid systems).
Research Activity Abroad
From the beginning of October to
the end of December, 2000, Dr. Simani was involved in research program/project
within the Control & Intelligent Systems Engineering research group of the
Department of Mathematics & Engineering at the University of Hull (with
Prof. Ron J. Patton).
His main research activity consisted
in the design and development of automatic fault detection and isolation
procedures regarding non-linear dynamic systems (industrial processes and power
plants, such as a model of a Continuous Stirred Tank reactor System and an a
real Sugar Factory process), modelling and identification of linear and
non-linear dynamic models, fuzzy modelling and control of linear and non-linear
algebraic and dynamic systems.
Dr. Simani is currently
collaborating with the Department of Engineering at the University of Ferrara
(Italy) within the Automatic Controls & Robotics Group (with Prof. Sergio
Beghelli and Prof. Cesare Fantuzzi) as well as the Department of Mathematics
and Engineering at the University of Hull (UK) within Control & Intelligent
Systems Engineering Research Group (Prof. Ron J. Patton) and working on other
national and international projects regarding the field of fault detection and
diagnosis in dynamic systems, dynamic system identification, fuzzy logic,
neural networks and non-linear system identification for fault diagnosis
(hybrid systems).
National & International Cooperative Projects
1995-1997 Cooperation project with the Science and
Supercomputing at CINECA. Title: “Visual Motion Estimation”: Development
of a Software for the Reconstruction of the Motion and the 3D Structure of
Objects from Monocular Digital Images. CINECA Computing Centre. Casalecchio di
Reno, Bologna (Italy).
1998-2000 Collaboration
with the University of Hull (UK) within the Network: EC FP5 Research Training
Network DAMADICS, “Development and Application of Methods for
Actuator Diagnosis in Industrial Control Systems”, Contract Number:
HPRN-CT-2000-00110. Network Co-ordinator: Prof. Ron J. Patton; host
institution: School of Engineering, University of Hull (UK).
2001-2003 Cooperation
project with the national companies GECO System (Geographical Environmental
Consulting) and HERA (Holding Energy Resources environmentAl) – Cesena (Italy)
for the design and result analysis of a model prototype for the estimation and
36 hours ahead prediction of the gas consumption in the northeast Italy.
2004-2005 Consultancy and cooperation project with the
University of Hull (UK, Prof Ron J. Patton), EADS - Astrium ESTEC (Toulouse,
France, Dr. Bernard Polle) and ESA (European Aerospace Agency, Holland, Dr.
Denis Fertin) with the title: “Robust Estimation for Failure Detection”, Ref :
EAA.TCN.89079.ASTR. Details: project for the development of a robust technique
for the FDI of the gyroscopes and thrusters of the Mars Express Satellite.
RESEARCH SUPPORT GAINED
Approximately 200.000 Euro from National
University projects, various industries, and local support from the University
of Ferrara.
One (1) Ph.D. student (2006-2008), four (4) undergraduate students (2006-2007)
and forty (40) final MS degree projects (1999-2006).
Publications
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