Engineering analysis dataset of instrument using Kohonen maps

(Self-organizing maps)

Master student of MMA department : Krasnykh K.N.Supervisor: Mylnikov L.A.

Issues in focus:•A large manufacturing enterprise •Tens of workshops•Some hundreds of instruments and

equipments

Issue: What one of the above is better? Effective? Profitable?

•To analyze all tools and instruments and make an adequate conclusion.

•To find out any hidden consistencies

Objectives: Practical implementation of the method to research it in the engineering environment and studying of its availability to the analysis of industrial data.

• Transformation of difficult multidimensional data to simpler structure of small dimension.

• Finding out of any hidden consistencies • Visualization and graphic representation of

outputs

The Kohonen’s networks and maps

Competitive training

Self-organizing maps

Clustering

Kohonen maps

Problem solving using the data of control and measuring devices & automatic equipment including two steps:

•1). Processing and clustering of objects by Kohonen's algorithm.

•2). Building and interpretation of Kohonen’s maps

Parameters:

•training set: 100%•conditions under which learning will end:

500 age or if the error is less than 0.05•Learning speed : 0,3 and 0,005•range of training: 6 and 0,1•Form of the neighborhood function :

Gaussian•The clustering algorithm: G-means

Input fields:

•1). Delivery time [80%] •2). Cost is low of the instrumentation

[100%]•3). Repair cost [90%]•4). Age of the instrumentation [60%]•5) Congestion levels the instrument [70%]•6). Uptime [90%]•7). Probability of failure [80%]

Kohonen maps

Kohonen maps:

Cost of the devices

Interpretation of results

Kohonen maps

Interpretation of results

Conclusion•Implementation of this method is actual

and effective.• Results of implementation:•Finding out of problems •Looking ahead of emergencies •Reduction of material service charge and

purchase of a new equipment•Choice of more effective and favorable

equipment

Thank you for your attention