Apply Prognostics and Health Management to Milling Machines
| Autor: | LIU, YEN-CHUN, 劉彥君 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The purpose of this study was to identify the use of Prognostics and Health Management (PHM) technology in milling machines. Upon PHM methods, health assessment of the spindle of the milling machine and the remaining useful life of the tool of the milling machine was discussed. Milling machines are indispensable tools in various manufacturing industries, and they are extensively used in many industries. Applying Industry Big Data Analytics, method of Fixed Cycle Features Test was utilized to collect the data from the sensor on the spindle of the milling machine. The results show that the remaining useful life of the spindle of the milling machine have about 211 and 195 working days respectively. Then, if the relevant maintenance is not executed, a failure without warn-ing may cause randomly. The other research of this study was the re-maining useful life of the tool of milling machine, the results show that the remaining useful life of each gradation have nuance difference be-tween two machine learning algorithms. Last, according to the operator's experience to judge that the tool had reached the service life, but the cal-culated health indicator of the tool was still about 0.2, which indicated the tool still have extra time for processing, and results show that there are still 17 minutes extra time for processing between two machine learn-ing algorithms. According to the results, Industry Big Data Analytics is an available method for health assessment and prediction of the remain-ing useful life. In the future, the model will be continuously updated to make the prediction more accurate. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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