Defect Detection Method for Self-Lubricating Sliding Bearing Coating Using Terahertz Total Variation Image Fusion

Abstract

Structural defects in self-lubricating sliding bearings would lead to local stress concentration and service life reduction. The absence of accurate detection technology for micro defects in self-lubricating coating has seriously limited their application in mechanical equipment. A total variation (TV) fusion terahertz imaging method is proposed toward the unidentifiable micro defect induced by overlapping terahertz echoes. First, a range of indicators is developed to quantify the alterations of signal characteristics in different stages of the defect. Subsequently, the TV fusion based on these indicator images could clearly identify different defects. After that, a multinomial regression model is established through the mathematical relation of defect thicknesses and indicators, thus the defect thickness could be obtained accurately. The experimental results for surface defect detection and internal delamination measurement demonstrate the high accuracy and excellent robustness of the proposed method, making it attractive for defect detection and quality assessment of self-lubricating coating.