| Summary: | 碩士 === 國立清華大學 === 化學工程學系所 === 106 === Infrared Thermography (IRT) is a popular method for Non-Destructive Testing (NDT). The principle of IRT is as follows. By applying thermal energy to the surface of the object under investigation, the surface temperature will change along time and show certain patterns that associate with the internal material properties. By recording the temperature values at each pixel with an infrared thermal camera, it can be judged whether there is a defect inside the object or not. However, the thermal images are usually disturbed by significant non-uniform backgrounds and noise. In addition, the thermal data are often numerous and contain redundant information. Therefore, thermal data analytics become a necessity.
In this study, the concept of Locality Preserving Projections (LPP) was applied to IRT. This linear tramsformation preserves local neighborhood information of the original data and at the same time ahieves dimensionality reduction. Its applicaiton to the thermographic data analysis field was named Locality Preserving Projections Thermography (LPPT). Next, a Sparse Principal Component Thermography (SPCT) method was proposed, which was enlighted by Sparse Principal Component Analysis (SPCA). Experiment results show that SPCT outperforms Principal Component Thermography (PCT) because it leads to more sparse loading images which are easier to be interpreted. At last, Independent Component Thermography (ICT) was also proposed in this work, which applies Independent Component Analysis (ICA) for detect detection based on thermal images. Based on the concept of blind source separation, ICT tries to separate the signals corresponding to normal and defective regions. The kurtosis statistic is used as an index to measure the non-Gaussionity of the signals. The Independent Components (ICs) with higher kurtosis values have larger chances to contain extreme values. In other words, they have higher probabilties to detect the existence of defects.
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