In the production and packaging of silicone sealants, entrapped air, impurities, and foreign particles can introduce defects affecting performance. This study utilizes three ultrasonic non-destructive testing techniques: contact-based, angle-beam, and through-transmission testing, to identify defects and generate 3D images. The scanning process takes vertical A-scan measurements across the sample and rotates it at predetermined angles for comprehensive coverage. The contact-based technique uses the time-of-flight principle to determine defect locations, but struggles with defects aligned perpendicular and experiences signal reduction. The angle-beam method identifies defects in areas previously out of reach, but the slow sound movement in sealants can hinder capturing specific signals. While through-transmission offers enhanced signal clarity and an improved signal-to-noise ratio, pinpointing the defect’s exact depth is challenging. By combining these methods, the study reconstructs a more accurate three-dimensional image which visualizes the defective region.
Data collected in active infrared thermography (AIRT) experiments for non-destructive defect detection in materials are often contaminated by undesired noise and backgrounds. In this study, an AIRT data processing method, which adopts adaptive fixed-rank kriging, is proposed. This approach computes a set of ordered functions that represent data features at the different resolution levels, called multi-resolution spline basis functions. Multiresolution spline functions were extracted from the thin-plate splines and ordered by the degree of smoothness. The only tuning parameter for this method is the resolution level, making this approach extensively applicable. The performance of the proposed method was evaluated by conducting a mosaic sample defect detection. The results showed that the proposed AIRT data processing method is not only efficient but also effective.
Defect detection is of great significance for assessing and controlling the quality of fabrics. However, most traditional detection processes rely on manual visual inspection, resulting in low detection efficiency, ambiguous detection results, and high monitoring costs. In this work, a centroid warp-weft graph-based (C2WG) statistical analysis method is proposed for the detection and evaluation of fabric defects. To reflect the fabric texture variation, the C2WG method is first proposed to find abnormal texture centers. Subsequently, by dual monitoring of local slope and curvature, the location of the abnormal centroid can be accurately determined as texture defects and displayed. Finally, the defect evaluation results under different detection accuracy are obtained by changing the monitoring threshold. Consequently, the defects are classified into different classes. A case study on an industrial design fabric product validates the good performance of the proposed method.
In some large-scale industrial production processes, when a fault occurs in a unit, it will spread through the connectivity between the units, which can affect the entire factory and cause product quality deterioration or even more serious problems. It is important to diagnose and isolate the root cause. Granger causality analysis is widely used which provides an effective way to localize root cause of faults. However, the conventional Granger causality is not suitable for nonlinear and high-order signals. This paper proposes an effective model-free, copula-based Granger causality method for root cause diagnosis of plant-wide oscillation which can effectively reveal the nonlinear and high-order causality. Granger causality is transformed into log likelihood ratio of conditional distribution and conditional copula is used to derive an effective estimation. The numerical simulation case prove the validity of Granger causality analysis based on copula function. At the same time, for the root cause diagnosis of the actual plant-wide oscillation, this method also successfully detected the correct root cause.
Conference Committee Involvement (4)
Digital Twins, AI, and NDE for Industry Applications and Energy Systems 2025
18 March 2025 | Vancouver, Canada
NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II
25 March 2024 | Long Beach, California, United States
NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE
14 March 2023 | Long Beach, California, United States
NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World
8 March 2022 | Long Beach, California, United States
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