馬達在現今的科技發展中扮演著不可或缺的角色。若因馬達損壞造成機器故障或是產線停擺，其損失將無法估計。所以馬達的故障檢測與預防診斷技術將越受重視。現今馬達檢測方式主要又分為振動檢測(Vibration analysis)與馬達電流特徵分析MCSA(Motor Current Signature Analysis)。振動檢測技術雖然能立即察覺機器異常，但主要為局部性的損壞觀察。其檢測方式為侵入式之檢測，且在低轉速時較難觀察出異常。MCSA方面則可全面性觀察馬達故障，其檢測方式為非侵入式，且價格較振動檢測低廉許多，所以現今MCSA技術越來越受到業界歡迎。
本研究使用振動分析及電流特徵分析觀察轉動設備軸承隨時間的磨耗情形，並從電流訊號中挑選符合振動趨勢的特徵，以建立回歸方程式模型，並從中推估馬達軸承的狀況，在機器壞損前達到提前預防之動作。

The motor has played an indispensable role in the technological development since the Industrial Revolution. The motor damage or motor failure would cause the shutdown of the entire production line and result in huge loss. Therefore, the motor fault detection and preventive maintenance will attract more and more attentions. Nowadays, the motor detection methods are mainly divided into vibration analysis and MCSA (Motor Current Signature Analysis). Although the vibration analysis can detect the abnormality of the machine immediately, it is an intrusive detection method mainly for local damage observation. It is difficult to detect the abnormality at low rotation speed. On the other hand, the MCSA is non-intrusive and is more comprehensive in observing motor faults. Its price is much lower than the vibration detection. Therefore, MCSA technology has become more and more popular in the industry.
In this paper, the vibration and current analysis are used to observe the bearing wear condition of the rotating equipment over time. Moreover, the characteristics of the vibration trend not only are selected from the current signal to establish the regression model, but are also used to estimate the condition of the motor bearing to achieve pre-emptive action before the machine is damaged.

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