This study examines the feasibility of the image deep learning method using convolution neural networks (CNNs) to maintain a porcelain insulator. Data augmentation is performed to prevent over-fitting, and the classification performance is evaluated by training the age, material, region, and pollution level of the insulator using image data in which the background and labelling are removed. Based on the results, it was difficult to predict the age, but it was possible to classify 76% of the materials, 60% of the pollution level, and more than 90% of the regions. From the results of this study, we identified the potential and limitations of the CNN classification for the four groups currently classified. However, it was possible to detect discoloration of the porcelain insulator resulting from physical, chemical, and climatic factors. Based on this, it will be possible to estimate the corrosion of the cap and discoloration of the porcelain caused by environmental deterioration, abnormal voltage, and lightning.

In an attempt to estimate the life projection of LED packages, IESNA published a paper regarding an LED package measurement test method in 2008, and a life projection technical document in 2011, to be used for LED life estimation. IESNA’s publications regarding LED package measurement methods were functional, but they were not internationally standardized before 2017. In order to develop a standardized method, the International Standard chose to use the LM-80 as a measurement method for LED life projection in their publication in 2017. Many projection methods have been discussed by the IEC Technical Committee 34 working group, including the method using an exponential function, which reflects lumen degradation characteristics well. This study is designed to explore alternative LED package life estimation methods using an exponential function with statistical analysis, other than the one suggested by the International Standard.