本研究提出了一個基於無感測器的並聯式機械手臂故障偵測方法，在不加裝額外感測器的前提下，僅需量測馬達驅動器、編碼器及扭矩之資訊，再搭配比例‒微分控制器(PD controller)即能偵測系統故障，並實作Delta手臂進行驗證。首先建立Delta手臂的運動學及動力學模型，接著使用傅立葉級數軌跡及最小平方法識別其動力學模型參數。本研究假設在正常狀態下所識別之參數為常態分布(normal distribution)，一旦系統發生故障，則其相對應的參數分布將會偏離99%的信賴區間，並以t檢定的P值量化參數在正常及異常分布下的差異，達到故障偵測及識別(fault detection and identification)的效果。實驗證明本方法可在不需要事前蒐集故障案例的情形下，即可成功的偵測Delta手臂的質量不平衡及球窩接頭磨損，並量化其嚴重程度。

This research introduce a method to detect the fault based on the sensorless delta robot under the premise of without additional sensor. the information of the motor driver, encoder and torque are needed to detect the fault of the system with the PD controller, including the practical verification, first of all, create the dynamic and kinematic model of the Delta robot, then using least square method and fourier series trajectory to identify the parameter of the model. In this research, we made the assumption that the parameter which is identified under normal condition is in the normal distribution. once the fault is occur in the system, the related distribution of the parameter will fall outside 99% confidence interval, by using t-test to find out the p value which can be used to quantized parameter under normal or abnormal distribution, the goal of fault detection can be reached. from the experiment, we proof that it is possible to detect the mass unbalance and the wear of the ball and socket joint and quantized the level of the severity without collecting the fault cases before hand.

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