1. Engineering analysis dataset of instrument using Kohonen maps (Self-organizing maps) Master student of MMA department : Krasnykh K.N. Supervisor: Mylnikov L.A.

2. 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

3. 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

4. Competitive training Self-organizing maps

5. Clustering

6. Kohonen maps

7. 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

8. 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

9. 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%]

10. Kohonen maps

11. Kohonen maps:

12. Cost of the devices

13. Interpretation of results

14. Kohonen maps

15. Interpretation of results

16. 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

17. Thank you for your attention