Prediction method of equipment maintenance time based on deep learning

Xiaoling Luo; Fang Wang; Yuanzhou Li

doi:10.1117/12.2575725

5 November 2020 Prediction method of equipment maintenance time based on deep learning

Xiaoling Luo, Fang Wang, Yuanzhou Li

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650M (2020) https://doi.org/10.1117/12.2575725
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

In the actual maintenance support application, it is difficult to carry out maintenance work according to the actual environment and conditions of the equipment to be repaired, which leads to the inaccurate timing of the equipment and the low efficiency of the equipment use. In order to study the relationship between the time interval of equipment repair and the geographical environment of the equipment more deeply, a RCNN deep learning model is proposed for training and feature extraction of equipment maintenance service information and geographical environment information, which can also predict the equipment maintenance interval. It also helps to compare among different prediction models and verify the effectiveness of the model, which further provides a method for the optimization of equipment maintenance interval.

Citation Download Citation

Xiaoling Luo, Fang Wang, and Yuanzhou Li "Prediction method of equipment maintenance time based on deep learning", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650M (5 November 2020); https://doi.org/10.1117/12.2575725

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