RISKMAN – Subproject proposal
1.1.
Subproject full title:
New Concept of Diagnostic Dynamic Expert Systems
1.2.
Subproject Acronym:
DIAGES
1.3.
RISKMAN Research Area:
RA2: Systems Performance Monitoring and Diagnostics
2.1.
2.2.
Proposing Organisation: Institute of Fluid Flow Machinery of the Polish Academy of
Sciences, Gdansk, Poland
Contact Person Name:
Prof. Jan Kicinski
2.3.
Address:
2.4.
Tel.:
(+48) 58 341 12 71
2.5.
Fax:
(+48) 58 341 61 44
2.6.
e-mail:
kic@imp.gda.pl
2.7.
Web site:
www.imp.gda.pl
2.8.
Participating companies, name and country:
Fiszera 14, PL – 80-952 Gdansk, Poland
1. (IMP-1) Institute of Fluid Flow Machinery – Department of Thermomechanics of
Fluids, (O2),Gdansk, Poland, Leader: Prof. P. Doerffer e-mail: doerffer@imp.gda.pl
2. (IMP-2) Institute of Fluid Flow Machinery - Department of Machine Mechanics,
Gdansk, Poland, Leader: Prof. J. Kicinski e-mail: kic@imp.gda.pl
3. (SUT-1) Silesian University of Technology- Department of Machinery Design,
Gliwice, Poland, Leader: Prof. W. Cholewa e-mail: wch@polsl.pl
4. (SUT-2) Silesian University of Technology- Department of Power Systems, Gliwice,
Poland, Leader: Prof. T. Chmielniak e-mail: imiue@rie5.ise.polsl.gliwice.pl
5. (UMM) University of Mining and Metallurgy, Department of Vibroacustics and
Robotics, Krakow, Poland, Leader: Prof. T. Uhl e-mail: tuhl@rob.wibro.agh.edu.pl
6. (TUG) Technical University of Gdansk, Department of Ship Automation and
Turbine Propulsion, Gdansk, Poland, Leader: Dr J. Gluch, e-mail: jgluch@imp.gda.pl
7. (MD) Machinery Diagnostics (SME), Gdansk, Poland, Leader: Prof. A. Gardzilewicz
e-mail: gar@imp.gda.pl
8. (EC) Energocontrol sp. Z.o.o., Krakow, Poland, Leader: Dr. T. Barszcz, e-mail:
info@energocontrol.com.pl
3.
Proposal summary
The main goal of the proposed subproject is the formulation and application of
knowledge- and model-based artificial intelligence methods such as: heuristic
modelling, multi-models approach, model inversion techniques, adaptive systems,
fuzzy logic methods, neural networks methods, genetic algorithms methods and their
combination to the monitoring and control of industrial systems.
1
A new concept here consists in construction of a system in which generated
conclusions depend on the time available. The longer the available time, the more
reliable are the conclusions. This is the main idea of, so-called, dynamic expert
systems. The model reversing technique as one of the methods of knowledge
acquisition plays an important role in this approach.
Other new concept of intelligent sensor will be formulated and developed. The
intelligent smart sensor (ISS) will have several flexible inputs from primary sensors as
accelerometers, pressure sensors, and force sensors. The sensor is designed with the
use of artificial neural networks or fuzzy logic intelligent algorithms depending on
application and implemented hardware.
DIAGES
Diagnostic Dynamic Expert System
RTD Tasks
Dynamic
Diagnostics
Thermal
Diagnostics
Intelligent
Smart
Sensor (ISS)
DEM Tasks


Prototype of DIAGES – System
Demonstration on virtual plant
 Possible implementation in real power plant
TRA Tasks
Training of personnel
involved in operation
of DIAGES
Fig.1. General DIAGES subproject structure
The application of these tools on industry level is the main breakthrough idea of
the proposal presented here. Development of these tools needs sound "knowledge
base" concerning the test object, which is a thermal power plant.
Present power stations should be converted into energy factories of the future
making use of such diagnostic systems leading to appropriate organisation models.
The general structure of the DIAGES subproject is presented in Fig.1.
2
4.
Objectives
Design and implementation of a low cost, intelligent data storage diagnostic units for risk
management algorithms, running on higher levels of distributed diagnostic networks, is the
general objective of the subproject. To achieve such a goal the following group of objectives
has to be realised:
- formulation and implementation of modern knowledge- and model-based approach to
secure and predictive maintenance of power industry machinery,
- measurement data reliability determination,
- development of new concepts of knowledge acquisition for supervisory systems,
- development and implementation in industry of the procedures for better performance
and safety of machinery taking into account the mechatronic and micro-devices
aspects,
- modeling methodology of selected objects (e.g. model reversing using artificial
intelligence, modeling of couplings, heat exchangers, etc),
- creation of knowledge basis and expert systems for industry,
- optimisation of the decision making by genetic algorithms,
- development of communication tools according to future standards (UMTS),
- development of first level diagnostic algorithms for ISS subsystems,
- development of data compression algorithms according to risk management task,
- development of self diagnostic actions algorithms,
- development of hardware solution of ISS coupler according reliability and data safety,
- determination of relations between reasons and symptoms using new methods of fuzzy
logic, genetic algorithms and artificial neural networks.
These objectives are aimed at improvement of our products according to risk management
related tasks. The module of this diagnostics should be a part of an open dynamic and hybrid
diagnostic system.
Access to necessary results of measurements is possible in cooperation with industrial
partners and SME.
5.
Deliverables (new products, new processes and services, radical innovations; prime deliverable is
expected to be a breakthrough in applicable knowledge to be transferred to industry and society)
D1)
New dynamic expert system ready for operation in the plant (full
documentation of the system, prototype hardware and software environment)
D2)
Prototype modules of knowledge base and fusion software.
Complete set of routines for dynamic and thermal diagnostics based on
artificial intelligence and new updatable knowledge base (documentation).
D3)
Prototype modules of system models and diagnostic multi-models
(documentation, models of dynamical states, model of heat exchangers
degradation, computer codes)
D4)
New algorithms for first level diagnostic tasks implemented in ISS (Intelligent
Smart Sensor) and/or ISS coupler
D5)
Prototype of communication module for ISS coupler using new
communication standards
D6)
New on-line and off-line learning algorithms for ISS.
3
D7)
Prototype of monitoring system using developed ISS (several different units)
and ISS coupler installed on industrial or infrastructural object
D8)
www nano-server embedded in ISS coupler. Host computer-supervising
installation of all ISS components.
D9)
Procedures for thermal diagnostics based on entropy methods.
D10) Trained neural networks for all new modelling methods
D11) Satisfactory tests of system operation on virtual heat cycle
D12) The set of algorithms for evaluation of DIAGES impact on environment
D13) Final report, conclusion and operation
6.
Justification and potential impact (economic impact, direct and indirect economic benefits,
European dimension, training and education, conformity with EC societal objectives: quality of life,
health, safety, working conditions, employment and environment)
Main types of energy necessary for human society consist of electricity and heat (or chill).
Sufficient amount of energy is produced by continuously operating power plants. District
heating power plants are much more efficient sources providing energy than individual
heating systems. Besides, the excess energy in power plants is used to generate electricity,
increasing the economic quality of the system. Usually they are placed in the neighbourhood
of towns and other centres of civilisation. This close neighbourhood causes many problems,
not only technical but also social. Technical problems concern efficient energy production,
safety for the crew and other people, lifetime of the components etc. while social problems
concern enterprise’s organisation, environment protection, wastes utilisation, communication,
etc. Nowadays each of these problems is solved individually and in many cases improvement
at one place causes worsening at the others. The new procedures for such enterprise
combining all the problems, applying more sophisticated and knowledge-based concepts are
needed in order to fulfil requirements not only known nowadays but also to answer new
demands of the future. That should be solved not only during the design process but also
during exploitation under continuously changing demands and conditions. Diagnostic systems
based on modern methods, making use of artificial intelligence, provide the best solution for
new concept of cheap (efficient), flexible, integrated, safe and clean energy production.
Broad definition of the diagnostics covers different fields of application. Vibration
diagnostics, foundation diagnostics, material and life-time diagnostics, control systems
diagnostics help to assure safety of production. Thermal (or performance) diagnostics let to
decrease internal and external production costs mainly by reduction in consumption of
primary resources and by improvement of environmental protection against pollutants.
Diagnostics of measuring systems helps to provide reliable experimental data. Economical
and organisational diagnostics assures overall low cost production. All these diagnostic
systems applying artificial intelligence methods, heuristic modelling, multi-models approach,
model inversion technique, training of adaptive systems, fuzzy logic methods, neural
networks methods, genetic algorithms methods and new concept of intelligent sensor should
cooperate. Distributed systems theory known from computer technology and from systems
theory let to combine them efficiently and to create open dynamic and hybrid supervising
system. Those system lets to take into account not only known but also new unknown
production conditions and technologies and thus it is flexible and easy to develop. However it
is a very challenging task taking into account its complexity. Present power stations should be
converted into energy factories of the future making use of such diagnostic systems leading to
appropriate organisation models.
4
7.
Description of the work (technological approaches and methods, work tasks, their description,
deliverables and work effort in person month) marked according to the following components (RTD =
Research, technological development and innovation-related activities, DEM = Demonstration
activities, TRA = training)
Dynamic diagnostics. Expert system integration
All the developed modules are to be integrated into a dynamic diagnostic expert
system. The expert system will take advantage of data, knowledge and information fusion
from different sources. As data sources both the internal and external (distributed) databases,
model-based data sources as simulation units, and measurements from multiple sensors will
be used. This task addresses development of an expert system able to operate in full dynamic
regime with taking into account dynamics of the system being diagnosed or monitored. By
the development of the system under consideration the broad experience of the consortium in
implementing of such a class of expert systems operating in industrial conditions will be of
great advantage.
The main principle of the system structure consists in its open character since it will be
possible to include into the system other modules, which were developed for the sake of other
systems and by other RTD groups.
Heuristic modelling is applied to identify models from data that describe behaviour of
dynamic objects and processes operated by humans, mainly in open control loop. The models
are especially useful in such cases where both continuous and discrete, event-type controls
and uncontrolled inputs to the object/process occur. Regardless heuristic nature, they make
possible the accurate predictions of system/process parameters. The objective of the models
may be manifold.
History of w1...wn values
Input
supervisor
model 1
 w1
model 2
 w2
Output
...
 wn
model n
Multi-models are aggregates of several different models whose inputs are fed to the
common multi-model input, whereas the multi-model output is combined from outputs of
constituting models –Fig.2. There are many solutions to a way of the outputs combination.
One of them being their ordinary, linear combination. The diagnostic multi-model
corresponds to a negotiating team of experts, where each component model corresponds to an
expert, a model’s output may be interpreted as an expert’s opinion. The multi-model output
corresponds to the opinion of the team of experts, and weights may be interpreted as experts’
competences. Such multi-models are going to be applied in the expert system.
The essence of data and information fusion consists in gathering information at the
same time or at some intervals from many data sources. This information enables better
5
identifying technical state of the examined object. Precision and reliability that may be
obtained using the Data Fusion methods could not be achieved using standard manners (with
single sensors). Methods of data and information fusion are to be implemented among dataand knowledge sources, yielding required integration between units of the expert system.
Diagnostic expert systems use different knowledge representations and reasoning algorithms.
An important branch of research dealing with modelling diagnostic rules is connected with
Bayesian networks, introduced (or reintroduced) by J. Pearl, called also belief networks (BN).
The most important step in the designing of the BN is connected with acquisition of
conditional probabilities. They may result from different sources, e.g. such as known physical
or numerical models, results of experiments or passive observations as well as opinions of
domain experts. An expert opinion is often the only source of information on the discussed
probability. The quality of the BN depends on the quality of network structure and parameters
introduced by experts. It should be explained that the quality measure of such data is
unknown and validation of the complex BN, which use such data is very difficult or even
impossible.
Diagnostic model of an object describes the relations between observed symptoms and
their causes, i.e. technical states of the object. It exemplifies a class of inverse models i.e.
models determining the matching results to causes. Direct specification of such models is
(quite) impossible due to complex nature of these relations. We propose to deal with the
general idea of the inverted diagnostic models, i.e. models obtained as the result of a kind of
inverting transformation applied to known cause-effect models. Development and
demonstration of new methods for the efficient definition, identification and updating of
inverted models is one of important goals of the subproject.
Thermal performance diagnostics
The need of thermal performance diagnostics emerges from the new and rational tendency
in power unit operation consisted in elongation of periods between repairs. This tendency
formulates serious task for performance diagnostics whose role is to keep power station at the
highest possible efficiency and economic level. That diagnostics assures low consumption of
primary resources which in turn influence economics, safety and eventually decreases
environmental impact of energy conversion by thermal power plant. It helps also to make
optimisation of energy conversion by whole energy production system. In this new approach
risk based analysis is necessary, leading to optimal decision-making for maintenance planning
as long as off line aspects are concerned, but also on-line decisions connected with safety
issues.
Analysis of heat-flow cycle makes use of the diagnostic relations. These
relations are to be created with the use of new knowledge-based methods aiming at
application of the artificial intelligence methods. They help to recognise the faulty
(inefficient) components of the system as well as scale of their degradation. Such a diagnostic
system gives data for economically founded innovative, reliable, smart and cost-effective
exploitation procedures. This system on the one hand is based on expert knowledge and
intelligence and on the other hand it develops this knowledge, verifies it and transfers new
experience into these procedures. Moreover it supplies knowledge from the exploitation to the
design, helping in a creation of more and more efficient life-cycle systems.
Application of knowledge-based artificial intelligence methods consists in:
 Fuzzy logic methods,
 Neural networks methods,
 Genetic algorithms methods,
 Application of combination of these three.
6
It is believed, that smart and efficient system should apply all of this methods for
particular purpose. For example fuzzy logic methods could supplement diagnostics of
measurement lines and devices. Neural networks make calculations easier and they are
efficient in searching of faulty components and in determination of degradation scale for
objects operating under known conditions. Genetic algorithm method helps to find inefficient
components for objects operating under unknown or newly established conditions. All these
actions are the base for technical, economical and environmental analysis. Thus, this system
could be treated as hybrid system and it needs strong cooperation between industry and
researchers in order to be reliable. This helps them also to exchange knowledge.
The system developed in this way is an open system which could include new ideas and
procedures as well as be a part of a supervising distributed system creating appropriate
organisation models. It could be used as well for operation as for training purposes. It
improves knowledge based management and it could be a part of industrial systems of the
future.
Intelligent Smart Sensor (ISS)
The intelligent smart sensor (ISS) will have several flexible inputs from primary sensors
e.g, accelerometers, pressure sensors, force sensors, voltage or current. The sensor is designed
with the use of artificial neural networks or fuzzy logic. Intelligent algorithms depend on
application and implemented in hardware. For hardware implementation FPGA technology
and new design procedure will be formulated. Currently many diagnostic systems use vision
as primary input. Proposed solution of ISS is also capable to handle such inputs. Local ISS
coupler is a kind of hub for several ISS units. Together it creates real time monitoring network
for monitoring and first level diagnostics. The coupler is highly sophisticated embedded
computer. It performs the following tasks:
- data acquisition from ISS,
- data recording (in range of 1GB memory)
- management of ISS group e.g. Update of artificial neural network parameters inside ISS
using chosen learning algorithms
- sharing data on local www nano-server
- execution of the first level diagnostics algorithms
Notification about detected damage can be made available using wireless and remote
communication tools like; GPRS, SMS, radio.
A local autonomous diagnostic centre (unit) described above can be integrated into diagnostic
networks and can be applied for any devices (vehicles, power plants, ect.) or infrastructure
elements (tunnels, bridges, dams, buldings, ect.) Proposed system is fully modular and can be
installed as autonomous or fully integrated with diagnostic network
Implementation of dynamic diagnostic expert system
The goal of this task is to put the dynamic diagnostic expert system into operation by the
consortium. The intended expert system is going to be implemented in the Kozienice Power
Plant at turbine generator sets of 230 or 500 MW. To put the system into operation some
investment work is needed, that covers complete hardware including measuring lines,
transmission of signals and data, signal processing hardware and software, required computer
systems etc. Finally, the system will undergo extensive validation with the use of especially
elaborated validation software and techniques.
The possibility of realisation of this task depends upon the interest and the resources of the IP.
7
Training of personnel involved in operation of dynamic diagnostic expert system
To enable the industrial partner to operate the system into the plant, an extensive training is
intended to carry out. This training will take part with the application of the expert system
operating on data collected from the real-existing object. Moreover, training on the system
operating in several simulation modes will be applied. To make it possible, special data and
knowledge sources will be included into the system, and respective training facilities and
materials prepared..
Work split
Task
No.
1
2
2.1
2.2
3
3.1
3.2
4
4.1
4.2
4.3
4.4
4.5
Task description
Partner Deliverable
Expert system integration (RTD)
SUT-1 D1
Heuristic modelling of systems (RTD)
SUT-1 D2
IMP-2
Development of new models and computer codes IMP-2
describing the dynamical state of selected objects (RTD)
Verification of new models and codes both in laboratory IMP-2
scale and on real objects (RTD)
Diagnostic multi-models (RTD)
SUT-1 D3
IMP-2
IMP-1
Methodology of description of coupled aerodynamic, SUT-1
mechanic and electric interactions in large power industry IMP-2
objects (RTD)
IMP-1
Identification procedure of coupled multi-models (RTD) SUT-1
IMP-2
Thermal performance diagnostics (RTD)
IMP-1 D9
TUG
D10
SUT-2 D11
MD
Measurement data reliability determination.
IMP-1
Analysis of large sets of measurement. Determination of TUG
the possible reasons for their standard deviations. Code
preparation for artificial neural-networks to determine
reliability of measurements. (RTD)
Creation of knowledge base containing all elements of the IMP-1
whole heat-flow cycle. Preparation of codes allowing the MD
up-dating of knowledge base. (RTD)
Formation of a complete set of symptom – reason
IMP-1
correlations for all elements included in the analysis. IMP MD
PAN, MD (RTD)
Procedures for thermal diagnostics based on entropy
SUT-2
methods (RTD)
Preparation and verification of on-line diagnostic
TUG
procedures (RTD)
Effort
MM
15
20
30
87
8
Task
No.
4.6
4.7
4.8
4.9
5
5.1
5.2
5.3
5.4
5.5
6
7
8
9
9.1
9.2
9.3
9.4
10
11
Task description
Development of new modelling of heat exchangers
degradation (RTD)
Training of neural networks for all new modelling
methods (RTD)
Demonstration of system operation on a virtual plant,
(DEM)
Evaluation of new system impact on environment by
means of genetic algorithms (RTD)
Intelligent Smart Sensor (ISS) – (RTD)
Partner Delive- Effort
rable
MM
IMP-1
IMP-1
IMP-1
TUG
IMP-1
UMM
EC
Specification of complete system (RTD)
UMM
Design of product (RTD)
UMM
Prototype of system components (RTD)
UMM
EC
Integration of diagnostic system (RTD)
UMM
Demo installation (DEM)
EC
Data and information fusion for diagnostics and SUT-1
monitoring (RTD)
Acquisition of diagnostic knowledge (RTD)
SUT-1
Acquisition and improvement of diagnostic belief SUT-1
networks (RTD)
Identification and updating of inverted models(RTD)
SUT-1
IMP-2
Generation of training data (RTD)
IMP-2
Investigation of model sensitivity on selected defects IMP-2
(RTD)
Building and training of adaptive systems (RTD)
SUT-1
Building of diagnostic relations (RTD)
SUT-1
IMP-2
Implementation of dynamic diagnostic expert system
SUT-1
(DEM)
IMP-2
UMM
IMP-1
Training of personnel involved in operation of dynamic
SUT-1
diagnostic expert system (TRA)
UMM
D4D8 58
D3
10
D3
D3
10
10
D2
40
D1
D12
D13
60
10
9
8.
Partners involved, partner profiles (business idea, size, competence) and the role of each
partner
(IMP-2) Institute of Fluid Flow Machinery – Established as European Centre of
Excellence for Clean and Safe Technologies in Power Engineering Department of Machine Mechanics- coordination of the subproject,
research on dynamic diagnostics, building of large object models,
analyses of coupled non-linear vibration in rotor- bearing-foundation
systems, generation of training data for adaptive systems. High expertise
in model-based diagnostics, structural mechanics, crack identification,
FEM-Methods, industrial applications (e.g. new journal bearings for
turbo-sets). Size : 19 employers (4 Professors, 3 Assistant Professors).
(IMP-1) Institute of Fluid Flow Machinery - Department of Thermomechanics of
Fluids, thermal diagnostics, diagnostics of turbine flow path, monitoring
of heat cycle and intelligent analysis of its condition, searching faulty
components of heat cycle, heat cycle components degradation, research
on flow phenomena, artificial intelligence methods. 30 employees (1
professor, 4 assistant professors)
(SUT-1) Silesian University of Technology- Department of Machinery Design,
Research on dynamic diagnostics and expert systems (2 professors, 8
assistant professors, 10 PhD students)
High expertise in:
 diagnostics, development of diagnostic databases and their
implementation in diagnostics and monitoring diagnosing with hidden
models, identification and application of approximate and/or uncertain
models for technical diagnostic development of real-time dynamic
expert systems, diagnostics of complex machinery in varying conditions
of operations, model-based prediction of state propagation,
 industrial applications of diagnostic and intelligent monitoring systems
(eg. DT200 employed in the 200 MW Turbine Generator in a power
station in Poland – together with IMP2, SUT2, UMM, EC),
(SUT-2) Silesian University of Technology- Department of Power Systems,
(UMM) University of Mining and Metallurgy, Department of Vibroacustics and
Robotics. Research on Intelligent Smart Sensor, monitoring and
diagnostics systems, modal analyses. Size: 16 employers (3 Professors, 5
Assistants) and 12 PhD students.
(TUG )
Technical University of Gdansk, Department of Ship automation and
Turbine propulsion, Gdansk, Poland, Leader: Dr J. Gluch, e-mail:
jgluch@imp.gda.pl
(MD ) Machinery Diagnostics (SME), Gdansk, Poland, Leader: Prof. A.
Gardzilewicz e-mail: gar@imp.gda.pl
(EC ) Energocontrol sp. Z.o.o., Krakow, Poland. SME- high tech company, 40
employees, manufacturer of monitoring and diagnostic systems
10
including algorithms hardware and software, RTD performer,
mechanical design, mechanical integrity calculation, diagnostic service
performer.
9. Resources for total subproject and for each partner (resources needed: personnel, equipment
etc; costs, work effort in person month)
Partner Total
IMP-1
IMP-2
SUT-1
UMM
TUG
SUT-2
EC
(SME)
MD
(SME)
Total
10.
170.000
200.000
230.000
100.000
80.000
50.000
70.000
Direct
Overhead Travel Equipment Materials Personal MM
costs
136.000
34.000
4.000
10.000
3.000 119.000
61
160.000
40.000
5.000
15.000
3.000 137.000
70
184.000
46.000
5.000
10.000
5.000 164.000
83
80.000
20.000
4.000
10.000
3.000
63.000
33
64.000
16.000
1.000
3.000
1.000
59.000
30
40.000
10.000
1.000
2.000
1.000
36.000
18
56.000
14.000
5.000
2.000
49.000
25
80.000
64.000
16.000
-
5.000
2.000
57.000
30
980.000
784.000
196.000
20.000
60.000
20.000
684.000
350
Duration (starting date, duration in month)
1.01.2004, 36 months,
10. Financial plan
Years
DIAGES
2004
Total
12.
2005
330 000,-
330 000,-
2006
320 000,-
Other issues (e.g. ethical, gender, EC policy related issues)
Introduction of higher standards and new methods of plant control and monitoring will
increase the safety and efficiency of energy supply and the education standard of the crew,
which has an impact on society.
11