Experimental Determination of the Significance of Statistical Evaluation of Parameters Characterizing Secondary Diagnostic Indicators of Acoustic Emission

Statistical evaluation of secondary diagnostic indicators of acoustic emission (AE) is an integral part of signal processing after applying filtering methods. AE parameters of acoustic noise obtained while monitoring AE from two tools in the milling process are analyzed using the method of polynomial digital bidirectional filtering. The efficiency of this filtering method is examined by determining the difference between the original and filtered AE signals. Fragments of the information and noise components of the signal are separated to allow for experimental determination of the signal/interference indicator. It is shown that the use of the polynomial digital method of bidirectional filtering improves the quality of signal processing and makes it possible to detect statistically significant correlations between the parameters of AE signals when testing a defective and defect-free instruments. A linear regression model is applied to describe the ratio of secondary diagnostic indicators of a defective instrument to indicators of a defect-free tool during AE monitoring.

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