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Acoustic Emission (AE) data from single point turning machining are analysedin this paper in order to gain a greater insight of the signal statisticalproperties for Tool Condition Monitoring (TCM) applications. A statisticalanalysis of the time series data amplitude and root mean square (RMS) value atvarious tool wear levels are performed, &#65533;nding that ageing features canbe revealed in all cases from the observed experimental histograms. Inparticular, AE data amplitudes are shown to be distributed with a power-lawbehaviour above a cross-over value. An analytic model for the RMS valuesprobability density function (pdf) is obtained resorting to the Jaynes' maximumentropy principle (MEp); novel technique of constraining the modelling functionunder few fractional moments, instead of a greater amount of ordinary moments,leads to well-tailored functions for experimental histograms.Comment: 16 pages, 7 figure

Statistical properties of acoustic emission signals from metal cutting processes