電動機車運轉時會產生振動，長時間的振動會造成結構的疲勞損壞，而結構的振動資訊經常被用來判斷結構的狀態，因為不同結構的振動會有不同的振動模態。因此本文主要在設計一套電動機車的故障預測診斷系統，利用振動資訊快速檢測電動機車的狀態及預測可能發生之故障狀態。
本研究整合加速度規、訊號擷取模組及LabVIEW發展量測診斷系統，利用電動機車的振動訊號來診斷預測可能發生的故障問題，在檢測診斷之前需先找到特徵判別資訊，利用頻域、倍頻分析及統計方法找出到各種狀態之頻率特徵。本文針對馬達軸螺絲鬆脫、前輪軸螺絲鬆脫、避震器異常等故障狀態來做故障判別，依據狀態頻率特徵所找到的故障判別資訊，判斷電動機車是否含有故障訊號的特徵並告知故障類型，以達到預測診斷效果。
經由實驗結果顯示，診斷正常狀態下的準確率為93.33%、馬達軸螺絲鬆脫狀態的準確率95.56%、前輪軸螺絲鬆脫狀態的準確率90.56%及避震器異常狀態準確率為87.8%。就此結果而言，將狀態頻率特徵判斷應用在電動機車診斷上，可以有效診斷電動機車的狀態及故障種類。

Information on structural vibration are usually utilized to judge its status. Because, vibration on different structural will cause different vibration modes, even small differences. Electric scooter used in the process, the structural is likely to damage due to vibration fatigue. So, this research mainly focuses on designing a malfunction-predicting system for electric scooter in order to detect status of the electric scooter quickly and foresee a malfunction that may happen in the future.
An accelerometer and data acquisition module and LabVIEW are integrated to develop a measurement and diagnosis system in this thesis. By utilizing vibration signals from the electric scooter to diagnose the possible malfunction, before diagnosing, features are needed for identifying information. Frequency domain information, frequency multiplication analysis, and statistics are used to detect a certain status’s vibrational features. This thesis aims to address the screw problem of the motor shaft and front axle, shock absorber abnormalities, etc. The malfunction status is used to identify the fault based on the status frequency feature for locating the fault and identifying the information. Furthermore, it is evaluated whether the electric scooter has the malfunction signal feature to determine the type of fault in order to achieve a predictive diagnosis.
The experiment shows that the diagnostic accuracy of normal status is 93.33%, a loose screw in the motor shaft has 95.56%, a loose screw in the front axle has 90.56%, and a shock absorber abnormality has 87.8%. The results show that the frequency feature can effectively diagnose the status of an electric scooter.

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