Robust ultrasonic flaw detection using order statistic CFAR threshold estimators

Abstract

Ultrasonic nondestructive imaging of critical defects (such as voids, microcracks, in bulk materials is often hampered by the presence of interfering and ran d om scatterers (i.e., clutter or grain echoes) associated with the defect's environment. The detection of broadband ultrasonic echoes for NDE can be improved by using optimal bandpass filtering to resolve flaw echoes surrounded by grain scatterers. The optimal bandpass filtering is achieved by examiniig spectral information of the flaw and the grain echoes where frequency differences have been experimentally shown to be predictable. The flaw echoes can then be dwriminated by using adaptive thresholding techniques based on surrounding range cells. This paper presents Order Statistic (OSL Based processors (ranked and trimmed mean) to ro ustly estimate the threshold while censoring outliers. The design of these OS processors are accomplished analytically based on Constant False Alarm Rate (CFAR) detection. The OS Based CFAR detectors were evaluated using experimental data and their performance is compared with the Cell Averaging (CA) method. It is shown that the OS Based CFAR processors can detect flaw echoes robustly where the range cell used for the threshold estimate contains outliers.

Nondestructive Testing Method Based on the Spin Polarization Effect Template

“… Acoustic nondestructive testing methods are based on the parameters of elastic vibrations being recorded in their interaction with a tested item [17,18]. Acoustic signals are reflected in the control process from the material's defects and inhomogeneities, and their amplitude-time characteristics (arrival time, propagation velocity, and acoustic wave shape) are used to evaluate the quality of a test product [19]. These acoustic signals are recorded by appropriate sensors, usually in contact with the diagnosed wire surface [7].…”

“…The following analytical expression is the solution of the wave equation (18): cos( ) e FE    evolution coefficient of the spin induction wave process, reflecting the energy conversion efficiency of the harmonic oscillator into the wave process of the spin-spin field interaction;  w   cyclic frequency of the spin induction wave process at which max e    . In accordance with (19) for the spin induction flow, the following expression is true:…”