本研究的獨特性在於利用碎形理論建構動態即時處理系統，分析由磨耗試驗機取得訊號與磨潤系統行為之關聯性，根據W-M碎形方程式修正以往單獨以碎形維度Ds來代表訊號行為之模式，以二個碎形參數：碎形維度Ds與高度尺度參數G 來代表訊號行為。將即時處理技術與碎形理論結合，藉由碎形參數的變化找出原始訊號無法看出之細微變化及訊號亂中有序的規律性，與機台磨潤行為對應，同時探討潤滑油脂與負荷對於磨潤行為之影響。
磨潤實驗以Falex#6磨耗試驗機搭配四球磨耗機構來測試滾珠螺桿適用潤滑油脂的潤滑性質，實驗擷取摩擦係數、潤滑油脂溫度及接觸電阻等三種參數做為分析。計算不同實驗組別摩擦能量及量測四球磨痕寬度，探討不同油脂與工作負荷下之潤滑性質。再根據量測訊號之物理意義，建立訊號與磨潤行為之關聯，以訊號分析後的碎形參數來代表機台狀況。最終可以作為判斷機台運作狀況、試件表面磨耗情形之代表指標。經由實驗建立出不同油脂、負載下碎形參數與表面粗糙度之關連資料庫，作為日後使用機台時，對應實驗數據與試件狀況之參考。
根據實驗結果顯示，由分析接觸電阻訊號得到之碎形參數可以有效與表面粗糙度對應，以表面形貌儀進行磨耗點形貌掃描，得到接觸電阻變化趨勢與表面粗糙度存在一反比關係。而根據摩擦能量模式，摩擦係數處理後碎形參數的變化震盪幅度可以用來預測磨潤系統產生嚴重性磨耗行為可能性，透過本文建立之經驗判別式與即時處理系統，可以在發生嚴重磨耗前，偵測機台狀況提供提前預警功能。藉由訊號分析異常現象發生前後之訊號碎形特徵參數之差異性，與透過實務與理論分析互相驗證，最終建立異常現象時訊號的碎形特徵值的參考資料庫，此資料庫可作為機器監測時的判斷依據。
本文所建立的碎形理論即時處理系統做為基礎模組，可推展運用至各式機器設備訊號分析機制，建立異常現象時訊號的碎形特徵值的參考資料庫，提供業界作為機器運轉監測與預警之機制。

This study is unique in use of fractal theory building Real-Time systems to analysis signals from the wear testing machine. Obtaining the correlation between measuring signals and tribological behavior. According to the W-M fractal function model, using two fractal parameters: fractal dimension Ds and topothesy G to represent the signal behavior instead of the model which only using fractal dimension Ds. The combination of Real-Time processing technique and the fractal theory, fractal parameters can be identified which the original signal can not be seen from the subtle changes and orderly chaos signal regularity. In the same time, to probe into how lubrication grease and load affects tribological behavior of the machine.
All the tribological experiments in this dissertation are using Falex # 6 wear testing machine with a four-ball wear testing fixture. All these experiments are testing the grease’s lubrication properties which is used for ball screw. From this experiment, we can obtain friction coefficient, grease temperature and electrical contact resistance. By calculating the friction energy and the measurements of wear scar after different experiments, we can obtain the lubrication properties from different loads and greases. Then establish the correlation between signals and tribological behavior by the physical meaning of the measurement signal. And using fractal parameter which is calculated by fractal signal process to represent the machine status. Ultimately it may serve as an indicator which on behalf of machine’s operating status. Experimental set up by different greases and loads to build a related data base of surface roughness and fractal parameters for future use of the machine, and the corresponding between experimental data and the reference of specimen’s condition.
The experimental results show that the fractal parameter which is calculated from electrical contact resistance can effectively corresponding to surface roughness. By use of the surface topography instrument, we can measure the width of the wear scar. Obtaining the trends of contact resistance and surface roughness is an inverse relationship. According to the friction energy model, the fractal parameter from the friction coefficient can be used to predict the possibility of the tribological system to cause sever wear behavior.
Through the Real-Time system and the discriminant which is established from this dissertation, we can detect the machine’s condition and provide an early warning set before sever wear happened. By analysis the differences of the signals before and after the occurrence of abnormal status, we can eventually build a reference data base to judge whether this machine is in good or bad working conditions.
This Real-Time system built by Fractal theory in this paper can be a basis module to be used to promote mechanisms. The establishment of abnormal fractal characteristics data base, can provide the industry as machine operation mechanism for monitoring and early warning.

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