Prediction and Diagnosis System for Malfunctions of Electric Scooters

• Main Authors: Shengh-SiangWang, 汪聖翔
• Other Authors: Heiu-Jou Shaw
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/31295519599960701891

碩士 === 國立成功大學 === 系統及船舶機電工程學系 === 104 === 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.