本研究量測垂直式滾珠螺桿試驗機之溫度、位移計、振動及扭矩訊號
來判斷機台運轉之狀況。其中，不同部位的振動訊號分別以快速傅立葉轉
換、碎形分析、希爾伯特-黃轉換以及多尺度熵方法進行正常潤滑及潤滑
油脂劣化狀態之特徵萃取。透過快速傅立葉轉換及希爾伯特-黃轉換，發
現馬達振動頻譜會出現齒輪嚙合頻率，軸承振動頻譜會出現不同元件的
損傷頻率以及受螺桿振動模態影響的高頻區間，螺帽振動頻譜會出現球
通頻率，因此針對滾珠螺桿各部位元件在機台運轉時產生的不同特徵頻
率，其對應之幅值可作為區分潤滑油脂劣化前後之特徵。藉由碎形分析，
可以得知碎形維度s D 值與訊號的密度有關，高度尺度參數G值與訊號的
振幅有關，高度尺度參數G值能在特定部位區別潤滑油脂劣化前後之振
動大小差異。多尺度熵方法可以計算出訊號在多尺度下之熵值，其結果可
以有效的辨別出潤滑油脂劣化前後之差異。最後將不同方法萃取出不同
部位之特徵輸入支撐向量機得到正常潤滑及潤滑油脂劣化狀態之分類邊
界，藉由該邊界能即時反應出機台運作時是否產生潤滑油脂劣化之現象，
達到機台即時監測及健康診斷的目標。

This study focuses on measuring the temperature, elongation, vibration and torque signal
from different components of the ball screw drive system to identify working state. Features
related to fault type of lubricant degradation are extracted from vibration signals by using
Fast Fourier Transform, Fractal analysis, Hilbert-Huang Transform and Multi-scale Entropy.
Through Fast Fourier Transform and Hilbert-Huang Transform, it's clear to show that the
characteristic frequency of different components can be identified. The spectra of motor
appear gear meshing frequency, the spectra of bearings appear defect frequency of different
components and high frequency range which affected by screw vibration mode, the spectra
of nut appear ball passing frequency. Thus, feature extraction is accomplished by finding
magnitude corresponding to characteristic frequency. In fractal analysis, there are two
important parameters called Topothesy and Fractal dimension, expressed as G and s D
respectively. In the case of monitoring the specific components of Ball screw drive system,
Topothesy is more significant than Fractal dimension to discriminate the normal-lubrication
state and lubricant-degradation state. Multi-scale Entropy is used to calculate the entropy in
multi-scale of signal. At specific scales, the result of Multi-scale Entropy can be used clearly
to discriminate the normal lubrication state and lubricant degradation state. Finally, the
decision boundary is able to obtain based on implementation of different extractors via
Support Vector Machine. Through the decision boundary, it's possible to distinguish the
working state is lubricant-degradation or not. The goal of online monitoring and diagnosing
can be achieved.

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