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Intelligent Production Control
Scientific Methods and Research
Information technology has achieved a leading role in the development of many
technology sectors. Production is no exception. Solutions developed within
information technology have proven usable and applicable in production control
systems.
At the same time, expectations and requirements for production control have
increased, and there is a growing need for new methods and algorithms that go
beyond traditional feedback control. Intelligent Automation requires the
replacement of deterministic control sequences by a common negotiation
scheme and individual decision algorithms.
Within IPC project, a simulation system will be developed in the satellite
Engineering Platform for modelling industrial architectures supplied by the
satellite Industrial Application, but here negotiation and decision-making will be
empty shells.
As knowledge on principles on these techniques, presumably mathematical, is
very underdeveloped and application experience in terms of controllability of
these algorithms is not available yet, the satellite Scientific Methods and
Research is intended to fill these shells.
Probably, many of the so-called soft computing methods, e.g. artificial neural
networks (ANNs), fuzzy logic, and genetic algorithms (GAs), can be exploited in
increasing the performance and accuracy of control and in predicting failures or
disturbances in processes, machines, or devices. Furthermore, hybrid methods
(i.e., combinations of ANNs with GAs) have proven to both perform and have
the sufficient ability to “learn” from the environment they are applied in, in order
to be more efficient.
The proposed activities of Scientific Methods and Research are organized in 5
work-packages.
WP1: Negotiation and Decision Making
In the IPC vision entities that control physical equipment (physical agents)
negotiate tasks to be done and make decisions based in their individual
knowledge while respecting the organisational goals. To achieve such an
objective, research in the principles of negotiation and decision-making has to
be performed.
The objective is to develop computing methods for Decision Analysis in the
shop floor. This will provide an intelligent service assurance by including
autonomous decision analysis algorithms with learning, adaptive and
optimisation capabilities. It will also provide detection and control of systems
failures, degradation or disturbances, and to restore the system to an ideal
state.
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Objectives
Decision Making involves Intelligence, Design, Choice, Implementation, and
Learning. We consider each one separately:
Intelligence:
Intelligence is required in the search for conditions that call for decisions (data
gathering and failures, degradation or disturbances detection).
Algorithms are needed to identify and define the problem (distinguishing
between symptoms and root cause problems).
Design:
Design is about inventing, developing, and analysing possible courses of action.
Research is required in
 Development of a set of self organising Decision Rules,
 Construction of a representative model for data gathering and failures,
degradation or disturbances detection,
 Generation of Evaluation Criteria,
 Generation of Alternatives,
 Development of a model (mapping) that combines Criteria and Alternatives,
taking into consideration interdependencies among them.
Choice:
Choice is selecting a course of action from those available.
Choosing requires
 Validation of the model in terms of consistency,
 Development of sensitivity analysis (what if) scenarios.
Implementation:
Research is needed in
 Developing optimised methods to allocate resources,
 Implementing the solution to the original problem.
Learning:
Learning is crucial.
In order to automatically improve service assurance performance, research is
needed
 To establish a knowledge base of previous cases,
 To devise matching criteria and algorithms in order to sustain a continuous
and adaptive learning environment.
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Research Areas and Applications
Research Areas and Applications are identified:
Asset Management of Intelligent Manufacturing Maintenance Systems and
Multiple Criteria Decision-Making:
This research is concerned with the analysis of data related to machine failures
and design and the development of Computerised Maintenance Management
Systems (CMMSs) that model the manufacturing environment, collect data in
real-time, perform relative comparisons and provide decision support to different
levels within industrial enterprises. Techniques incorporated based on the
Analytic Hierarchy Process (AHP), and Fuzzy Logic Control (FLC).
Analysis of existing data related to categorisation of failures and their eventual
link to preventive schedules.
Previous research has shown that documented Preventive Maintenance (PM)
schedules are not implemented because they are too static and they do not
reflect shop-floor realities and needs. Moreover, they are given a lower priority
than fire-fighting situations and therefore, they never get implemented. Analysis
of current maintenance practice will be carried out, in terms of data capture
related to fault reporting and preventive measures. Initial attempts to model
generic characterisation of failures and fault codes has led to the conclusion
that the majority of failures tend to be categorised into broad categories such as
mechanical, electrical, hydraulic, pneumatic, and software. However, it was
found that further sub-dividing of these categories will lead to industry and plant
specific classifications. For example, under mechanical failures there might be
axis drive, conveyor, gearbox, fixture, etc. These categories will be arranged
into a hierarchy of failure characterisation. For every failure event (signature)
there will be a measure of significance in terms of frequency (number of times)
and downtime (duration). These combined measures will help to assign relative
weights of importance to the different types of failures. Next, weights will be
used to prioritise PM schedules so that existing PMs can be ranked in an
adaptive way. Knowledge of methods to categorise preventive schedules as
well as failure modes will be important in designing a system linked to
preventive maintenance databases. Techniques related to data mining and
prioritisation will be incorporated.
Data visualisation and its effect on reliability and machine performance.
It is important to set adequate performance measures such as Mean Time
Between Failures (MTBF) and Mean Time To Repair (MTTR) in order to monitor
improvements in performance of assets (downtime) and skills of maintenance
engineers (repair). Research will be carried out to understand failures by
focusing on severity and rate of occurrence.
In terms of severity, a fundamental question is ‘What is failure downtime?’. In
other words, at what level is the deterioration of state considered to be
downtime? Data captured will provide guidance to identification of parameters
that need to be monitored in an automated approach. The usage of Condition
Based Monitoring (CBM) will be investigated in order to facilitate automated
data capture of selected parameters. In terms of rate of occurrence, the concept
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of real- time data capturing and simulation of developed models on-line will be
investigated. Programmable Logic Controllers (PLCs) will be used in this
process as deemed necessary. This will naturally lead to modelling and
simulation of a pilot machine or a manufacturing system, where a manufacturing
system production line is modelled and linked to a PLC (Programmable Logic
Controller). The manufacturing system will be modelled and controlled using a
Supervisory Control and Data Acquisition (SCADA) system. A pilot study for
developing such a model of one of the existing studied machines will be
performed, but here the novelty is to try to simulate injected failures into the
modelled system. Being able to monitor and accordingly respond in real time
will provide an asset management model that is adaptive to shop floor realities
and needs. As Charles Darwin has expressed it succinctly: ‘It is not the
strongest of the species that survives, nor the most intelligent, but the one most
responsive to change’. In understanding failures in terms of severity and rate of
occurrence, we are approaching a state of responsiveness. Also being able to
understand and be ‘in control’ of failure events is one step nearer to the
concepts of self-repair and self- maintenance; a desirable feature of Next
Generation Manufacturing Paradigms.
Trade-off between reactivity and performance.
To have a self-organising adaptive system it would be best to implement it in a
decentralised way, but with the overhead of massive communication. To have a
good performing system, it would be best to have a central place where all
information is available, and the best solution is calculated. (in case of
combinatorial problems (e.g. scheduling) this would mean to generate a huge
set of solutions). Time is a critical factor. In case of a system failure a reactive
system is necessary. In case of an up-and- running system, optimisation is the
objective.
Research is necessary to identify the right algorithms for the individual classes
of problems, and some methodology is missing to let the system be both,
reactive and performing.
WP2: Algorithms Selection and Development
A large number of classes of algorithms is available, such as Diagnosis
Algorithms, Optimisation Algorithms, Distributed Decision Making, Distributed
Scheduling, Multiple Criteria Decision Making Algorithms, Resource Allocation
Algorithms, Data Mining Algorithms, Artificial Neural Networks (ANN), Fuzzy
Logic Control (FLC), Genetic Algorithms (GA), Knowledge Management, CaseBased Reasoning (CBR), the Analytic Hierarchy Process (AHP), the Analytic
Network Process (ANP), and so on.
The task is to select and develop suitable algorithms according to the
architectural and operational requirements of Industrial Applications satellite.
WP3: Integration with IPC Engineering Platform
IPC Integration Platform will use technologies such as Programmable Logic
Control (PLC), Supervisory Control and Data Acquisition (SCADA) systems,
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Computerised Maintenance Management Systems (CMMS), Condition Based
Monitoring (CBM) and other.
Algorithms developed within the project have to be adapted and integrated into
the Engineering Platform.
Research and report in respect to systems behaviour and performance,
including measurements is also included in this work-package.
WP4: Dissemination
WP5: Exploitation
Cluster Management
Participants in the SMR cluster will typically become associated partners to the
IPC project. In principle, only two of the members of the cluster will be full
partners in IPC.
It is necessary to know the proposed effort of each partner in the cluster, to
prepare a calendar for the project, milestones and deliverables, and a budget.
Comments from the Industrial Platform cluster are essential at this stage.
IPC SMR distribution list
IDIT
Francisco Restivo (fjr@fe.up.pt)
Paulo Leitão (pleitao@ipb.pt)
UMIST
Ashraf W. Labib (ashraf.labib@umist.ac.uk)
Newcastle, ISRU Dave Stewardson (d.j.stewardson@ncl.ac.uk)
Christine Burdon (C.E.Burdon@ncl.ac.uk)
Tekniker
Iñaki Maurtua (imaurtua@tekniker.es)
Profactor
Georg Weichhart (Georg.Weichhart@profactor.at)
Paragon Ltd.
Dimitri A. Manolas (paragon1@otenet.gr)
Berlin, DAI-Lab
Sahin Albayrak (sahin@dai-lab.de)
IPC partners
IPA
Michael Hoepf (hoepf@ipa.fraunhofer.de)
Schneider
Armando Colombo (armando.colombo@modicon.com)
KUL
Paul Valckenaers (paul.valckenaers@mech.kuleuven.ac.be)
Francisco Restivo
2003-02-10