Fluctuation analyses for pattern classification in nondestructive materials inspection

Abstract

We review recent work on the application of fluctuation analyses of time series for pattern classification in nondestructive materials inspection. These analyses are based on the evaluation of time-series fluctuations across time intervals of increasing size, and were originally introduced in the study of fractals. A number of examples indicate that this approach yields relevant features allowing the successful classification of patterns such as (i) microstructure signatures in cast irons, as probed by backscattered ultrasonic signals. (ii) welding defects in metals, as probed by TOFD ultrasonic signals; (iii) gear faults, based on vibration signals; (iv) weld-transfer modes, as probed by voltage and current time series; (v) microstructural composition in stainless steel, as probed by magnetic Barkhausen noise and magnetic flux signals.