| Summary: | 碩士 === 國立臺灣海洋大學 === 機械與機電工程學系 === 107 === This study proposes a cloud tele-measurement technique on electromechanical systems, and uses the neural network algorithm to diagnose the performance of the electromechanical systems. In this study, the vibration, temperature and humidity sensors are mounted on the electromechanical motor, and the external braking device is used to provide different load states to simulate the operating state of the motor under different conditions. Next, use the single chip through the sensor to instantly measure the vibration, temperature and humidity information of the motor running. Moreover, analyze the motor operating status, classified as normal, abnormal, need to close the electromechanical system status by using bayesian neural network algorithm. Finally,a single chip is used to automatically upload the status analysis results of the electromechanical system to the cloud website server, so that the monitoring person can obtain the remote diagnosis of the electromechanical system through the cloud website information, and reduce the time and labor cost of obtaining information. The damage of traditional motor equipment is unpredictable. With the research experiment and method, just start the motor, the program use the neurological module to analyze the current motor status, showing normal, abnormal, and need to be closed. can control at the same time for multiple sets of equipment system status. Establishment of this model system can optimize the early warning judgment and decision-making according to the damage, greatly improve the management efficiency, and achieve the goal of intelligent and optimizing the productivity of the factory.
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