基於多尺度熵與支持向量數據描述之軸承故障診斷系統

Abstract

對於各類迴轉機械來說，旋轉機械、齒輪箱、旋轉刀具等等各類元件容易因為長時間的震動與磨損，產生軸承運轉上的問題。並且，實際在工廠機台運作時，時常是沒有人手進行資料採集以及分類的。因此需要設計一套能夠前處理以及分類無標籤資料的系統。本研究提出了一個以多尺度熵進行特徵抽取，以複數尺度向量數據描述進行軸承振動訊號分析的系統。本研究使用IMS軸承資料庫進行測試，實驗結果能夠準確在軸承出現異常時迅速判斷，提醒使用者軸承出現損壞。並且以此方法，可以實現非監督學習，自行前處理並進行分析。

For all kinds of rotating machinery, rotating machinery, gearboxes, rotating tools and other components are prone to bearing operation problems due to long-term vibration and wear. In addition, in the actual operation of the factory machine, there is often no manpower for data collection and classification. Therefore, it is necessary to design a system that can pre-process and classify unlabeled data. This research proposes a system that uses multi-scale entropy for feature extraction and complex scale vector data to describe bearing vibration signal analysis. In this study, the IMS bearing database was used for testing. The experimental results can accurately determine when the bearing is abnormal and remind the user that the bearing is damaged. And in this way, unsupervised learning can be realized, pre-processing and analysis can be performed by itself.