使用不同的因果性分析模型做為特徵抽取演算法

本研究 所使 用的葛 蘭傑 因果 分析仍 然是 以線性 的 自 回歸模 型(Auto regression Model)來進行時序訊號的建模，雖然在實驗的大部分案例中都取 得相當不錯的成果，不過在某些情況下仍然有進步的空間，而這可能是因為 迴轉機械系統的某些故障行為具有非線性的特性，所以在未來本研究或許可 以嘗試不同的建模架構，在因果性的變化可以反應元件異常的前提下，能夠 得到更好的結果。

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表附錄- 3 使用雙平板夾持對心後_四個軸承時域的因果性分析

軸承 1 號相對 軸承 2 號相對 軸承 3 號相對 軸承 4 號相對 軸承 1 號 N/A 0.0201 0.0221 0.0123

軸承 2 號

0.0425

0.0419

0.0347 0.0501

由前面三組實驗的結果可以發現不管是哪種方法，經過對心校正後，幾 乎任兩個軸承之間的因果性都有下降的趨勢，而整體的因果性總和更是減少 至對心前的一半不到，這個結果如同前面 2.2.3 節的猜想，減少了因為偏心 造成的額外受力，也會降低軸承間額外的因果性關係，所以透過這個實驗可 以確定經過對心後得到的分析結果會更忠實反映軸承因為磨損而造成的因 果性變化。另外，在 2.2.3 節中曾提到，本研究認為使用雙平板夾持來對心 可以達到和使用千分表對心差不多的效果，其中一個原因就是經過葛蘭傑因 果分析後，兩種方法對於最後整體的因果性總和都可以達到相同程度的改善。

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