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Self-Organising Maps for Illustration of Friction in a Pneumatic Cylinder
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology.
2005 (English)In: 9th Scandinavian International Conference on Fluid Power, SICFP’05, 2005, 80-81 p.Conference paper, Published paper (Other academic)
Abstract [en]

Friction exists in virtually every mechanical system. A great many models for prediction and simulation of friction exist. However, due to the high non-linear nature of friction, especially stick-slip friction, there exists a trade-off between simplicity and accuracy in the predictions, also due to difficulties in building an accurate model.

Here an approach to estimation of friction based on accumulated knowledge, using previous measurements and estimations based on these measurements, is discussed. In this approach, a special kind of neural networks, is used. The type of neural network used here is a Kohonen self-organising map. Results from the trained map are used to illustrate how friction relates to states in the pneumatic cylinder. The structure in the map resulting from the different states is also discussed, interpreted and illustrated.

Place, publisher, year, edition, pages
2005. 80-81 p.
Keyword [en]
Self-organizing maps, friction, pneumatic
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-12975OAI: oai:DiVA.org:liu-12975DiVA: diva2:17571
Conference
9th Scandinavian International Conference on Fluid Power, SICFP’05, Linköping, Sweden, 1st-2nd June, 2005
Available from: 2008-02-26 Created: 2008-02-26 Last updated: 2013-11-21
In thesis
1. Fluid Power Applications Using Self-Organising Maps in Condition Monitoring
Open this publication in new window or tab >>Fluid Power Applications Using Self-Organising Maps in Condition Monitoring
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Condition monitoring of systems and detection of changes in the systems are of significant importance for an automated system, whether it is for production, transport, amusement, or any other application. Although condition monitoring is already widely used in machinery, the need for it is growing, especially as systems become increasingly autonomous and self-contained. One of the toughest tasks concerning embedded condition monitoring is to extract the useful information and conclusions from the often large amount of measured data. The use of self-organising maps, SOMs, for embedded condition monitoring is of interest for the component manufacturer who lacks information about how the component is to be used by the system integrator, or in what applications and load cases.

At the same time, there is also a potential interest on the part of the system builders. Although they know how the system is designed and will be used, it is still hard to identify all possible failure modes. A component does not break at all locations or in all functions simultaneously, but rather in one, more stressed, location. Where is this location? Here, the collection of as much data as possible from the system and then processing it with the aid of SOMs allows the system integrators to create a map of the load on the system in its operating conditions. This gives the system integrators a better chance to decide where to improve the system.

Automating monitoring and analysis means not only being able to collect prodigious amounts of measured data, but also being able to interpret the data and transform it into useful information, e.g. conclusions about the state of the system. However, as will be argued in this thesis, drawing the conclusions is one thing, being able to interpret the conclusions is another, not least concerning the credibility of the conclusions drawn. This has proven to be particularly true for simple mechanical systems like pneumatics in the manufacturing industry.

Place, publisher, year, edition, pages
Institutionen för ekonomisk och industriell utveckling, 2008. 56 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1163
Keyword
Fluid power, pneumatic, self-organizing maps, condition monitoring
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-11127 (URN)978-91-7393-971-3 (ISBN)
Public defence
2008-03-28, A35, Hus A, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2008-02-26 Created: 2008-02-26 Last updated: 2009-05-19
2. Selection schemes and neural networks in adaptive real-time control: predictive simulation adaptive control
Open this publication in new window or tab >>Selection schemes and neural networks in adaptive real-time control: predictive simulation adaptive control
2005 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The vision of self-aware machines was the starting point of this work. The idea is a machine having knowledge about itself and its surrounding environment, being able to react to changes in the environment. To support this vision, a number of engineering disciplines need to be merged and the control strategy "predictive simulation adaptive control'', PSAC, developed.

The focus of the thesis is the synthesis of a number of algorithms and ideas from different engineering disciplines and fields. The main disciplines that the work is based on are simulation techniques, selection schemes and neural networks; all of these combined with the constraints imposed by the real-time demands of control systems.

Selection schemes, or optimisation algorithms, are introduced here and used directly for real-time control of the test system, a rod-less pneumatic cylinder. The selection process is primarily based on genetic algorithms and the outcome of numerous simulations of the system for different possible control signals.

Neural networks in general, and the version used here in particular, the Kohonen self-organising map, is widely used for classification and storage of information. Here it is used first to approximate friction in a rod-less pneumatic cylinder, and later on, possible ways to utilise this technique for condition monitoring are briefly discussed.

Real-time systems and programming are a necessity when designing modern control systems. From the real-time constraints, special demands are put on the implemented algorithms and ideas.

By bringing all this together, piece by piece, the vision comes a little bit closer. One step on the path, is the PSAC control concept proposed here. The control concept is successfully implemented and tested on a position servo consisting of a pneumatic rod-less cylinder controlled by on/off-valves.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 62 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1187
Keyword
Adaptive Control, Real-Time, Self-Organizing Feature Maps, Pneumatic, Neural Networks, Evolutionary Algorithms
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-29324 (URN)14645 (Local ID)91-85457-11-6 (ISBN)14645 (Archive number)14645 (OAI)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-21

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Zachrison, AndersSethson, Magnus

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