ESTIMATION OF AE PARAMETERS FOR MONITORING SPINDLE BEARING IN A DRILLING MACHINE USING MULTIPLE REGRESSION AND GMDH
Keywords:
Acoustic Emission, Drilling Machine, Multiple RegressionsAbstract
Among various methods of condition monitoring, Acoustic Emission monitoring is a better method for the early detection of failure. Defects that can occur in bearings should be detected as early as possible to avoid fatal breakdowns of the machines to which they are so critical. Present work involves studying the variation of AE signals acquired from spindle bearing housing of a Drilling machine for various cutting conditions. Simple functional relationships between the parameters were plotted to arrive at possible information on bearing condition. But these simpler methods of analysis did not provide any information about the status of the bearing. Thus, there is a requirement for more sophisticated methods that are capable of integrating information from multiple sensors. Hence, methods like multiple regression analysis and Group Method of Data Handling (GMDH) have been applied for the estimation of AE Counts and AE Energy. From the Experimental data it was observed that as the cutting condition increases there is an increase in the signal level of AE parameters. This is due to increase in load acting on the bearing at higher cutting conditions. Estimates from multiple regression and GMDH were compared and it was observed that, GMDH with regularity criterion gives better results.
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