本研究中為模擬實際滾珠螺桿之運動行為，製作環狀式類滾珠軸承試件且具有兩種不同的牙型係數(Groove factor)設計，除了鋼珠直徑相同，試件尺寸，內、外環粗糙度，鋼珠數量，間隔環數量與厚度皆不相同。運用訊號分析技術於磨潤試驗機之軸向負載與轉速設定，使各牙型係數最大接觸應力接近2.0GPa，以探討不同牙型係數與內、外環表面粗糙度對於磨潤行為及振動之影響。本研究之主要目的，為使用較創新及效率之方法來處理環狀式類滾珠軸承之振動訊號與磨潤行為之關係。此研究與以往最大不同之特點為應用實驗室所建立之訊號分析技術，配合實驗機台所量測之磨潤及振動參數加以分析，建立元件不同接觸位置之摩擦及磨耗與振動振幅和形態之間的關係。基因演算法(Genetic Algorithms)可解得許多以往不易計算之軸承重要參數，例如滾珠與間隔環、內、外環之摩擦係數以及滾珠與內、外環之接觸角。而振動訊號分析所使用的演算法有自適性拆解(Self-Adaptive Decomposition)、快速傅立葉(Fast Fourier Transform)及多尺度熵(Multi-scale Entropy)等方法。希望藉由訊號理論方法找出關鍵特徵訊號，釐清振動最重要的發生來源，且即時反應試件磨潤之運作狀況。
實驗擷取之溫度、摩擦扭矩、荷重訊號、滑動比與振動訊號為軸承各種施工條件與內、外環粗糙度設計不同配置所導致。此參數及現象之控制因子為牙型係數以及內、外環軌道表面粗糙度。因為牙型係數會改變滾珠與軌道之配合度，從而影響平均摩擦係數、滑動比、溫升以及振動訊號；內、外環粗糙度則會影響平均摩擦係數、滑動比、溫升、振動訊號以及磨耗量。在牙型係數0.52中，當內環粗糙度配置Ra值約200nm時，則會降低滑溜現象(Skidding)，且亦有較低之平均滑動比。而當外環粗糙度配置Ra值約250nm時，則會減少間隔環之磨耗損失量，且亦有較低之平均熵值與平均振動量。在牙型係數0.54中，當內環粗糙度配置Ra值約300nm時，則會降低滑溜現象(Skidding)，且亦有較低之平均滑動比，而當外環粗糙度配置Ra值約200nm時，則會減少間隔環之磨耗損失量，且亦有較低之平均熵值與平均振動量。
滾珠與間隔環之運動接觸行為為試件兩種振動參數以及間隔環摩擦係數以及磨耗量之主要控制因子。綜合實驗結果顯示，滾珠與間隔環之接觸有三種接觸位置中最高的摩擦係數及磨耗量；而振動之兩種參數值亦與此兩種磨潤參數形成正比之關係。目前摩擦、磨耗以及振動參數之實驗結果，均已建立與牙型係數以及內、外環粗糙度之數學模型。將來可望引用人工智慧(Artificial Intelligence)或深度學習(Deep Learning)等方法，建立出不同操作條件下之磨潤及振動訊號資料庫(Database)，並配合人工智慧及深度學習技術達成智慧化系統感測診斷之功能。

In this study, in order to simulate the behavior of the ball screw, the ball-bearing-like specimen is designed with two different groove factors. In addition to the same ball diameter, specimen size, inner and outer raceway roughness, the number of balls, the number and thickness of the spacer are slightly different. The maximum contact stress of each groove factors coefficient is close to 2.0GPa by using signal analysis technology. The main purpose of this study is to use an efficient method to study the ball bearing movement and tribological behavior. The most different in the study is using signal analysis technology with the measured parameters. Using the genetic algorithms can solve the parameters. Using the algorithm of signal analysis technology including self-adaptive decomposition , fast fourier transform and multi-scale entropy. It is hoped that the key feature signal will be found by the signal theory method, and the operation status of the machine will be reflected in real time, and the action of the maintenance machine will be carried out. In the future, it is expected to use artificial intelligence or deep learning to establish a signal database under different abnormal conditions to achieve intelligent system sensing and diagnosis technology.
In the experiment, it is found that the temperature , frictional torque, load, slip ratio and vibration signal are caused by the configuration of various bearing conditions. The control factors of the specimens are groove factor and surface roughness.
The groove factor controls the average friction coefficient, sliding ratio, temperature rise and vibration signal, because the groove factor will change the degree of cooperation between the ball and the raceway; the inner and outer raceway roughness will affect the average friction coefficient, sliding ratio, temperature, vibration signal and wear.
The results show that the groove factor 0.52, while the inner raceway roughness is disposed at an Ra value of 200 nm, the effect of skidding is reduced, and a lower average sliding ratio is also obtained. While the roughness of the outer raceway is placed at an Ra value of 250 nm, the amount of wear loss of the spacer ring is reduced, and the average entropy value and the average vibration value are also low. The results show that the groove factor 0.54, while the inner raceway roughness is disposed at an Ra value of 300 nm, the effect of skidding is reduced, and a lower average sliding ratio is also obtained, which improves the lubricating performance. While the roughness of the outer raceway is placed at an Ra value of 200 nm, the amount of wear loss of the spacer ring is reduced, and the average entropy value and the average vibration value are also low.

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