CNC Tool Signal Prediction for Machine Health Monitor with Upper-Lower Boundaries Using Long Short-Term Memory Networks
| Autor: | Wan-WeiLu, 呂萬瑋 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In machine processing we need to predict anomaly time precisely to prevent work piece broken. Among them, there are two main methods for tool anomaly predication. One is image detection, other is signal detection. Image prediction need to use camera, and the picture is required to clear and no noise. However, due to the working environment, it is very hard to setup camera and get the clear picture. Therefore, this paper will base on long short-tern memory network to predict the upper and lower boundary. The structure contains three parts, signal preprocessing, training of long short-tern memory network and calculate upper and lower boundary. If the signal exceeds to our boundary, it means the tool situation should be warned, or even change the tool instantly. Besides, the training time for long short-tern memory network is about 3 hours. The boundary that drawn from trained network model will adjust by the signal location and variety, prove that the method is functional. For signal preprocessing, the paper uses segmentation to calculate the amount of change for each segment to give the reference for signal upper and lower boundary. In addition, segmentation can also speed up our training period to meet the industrial require. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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