Enhancement of Defect Detectability in Pneumatic Pressure Equipment Using an Automatic Detection Technique in ECPT

Abstract

Abstract Pneumatic pressure equipment is an important component of wind tunnel test systems, which have been extensively applied in most modern industries. To guarantee the reliability and safety of pneumatic pressure equipment, a regular inspection is needed. To improve the inspections process, automating the process of identifying defects continues to receive attention in the research community. In this paper, in order to achieve automatic defect identification, an improved feature extraction algorithm in eddy current pulsed thermography is presented. The presented feature extraction algorithm contains four elements: data block selection, variable step search, relation value classification, and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of a thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental results are provided to demonstrate the capabilities and benefits (i.e., increased precision, reduced processing time) of the proposed algorithm in automatic defect identification.