Diagnosing and Predictive Maintenance Systems for Abnormal Behavior of Power Scheduling Loading and Its Applications to Robotics System

• Main Authors: Shih-Hsien Wu, 吳思嫻
• Other Authors: Han-Pang Huang
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/62724839070405019067

碩士 === 臺灣大學 === 工業工程學研究所 === 98 === Economic development is dependent on power supply. Production, livelihood, and government departments rely on continuous and steady power supply to proceed for economic activities. Through research and development of intelligent robots, design bottlenecks have emerged particularly in the scheme process of emotional sensors of modernized robots or electrical vehicles. Batteries supplied to robots discharge too quickly. Under unstable discharge conditions in a heavy-duty platform, reliable cycle-lifespan is shortened and cannot be assured. Abnormal behavior may influence robot’s demonstration time. Thus, awareness of the battery status and the time for charging are important. In order to save power and promote efficiency, steady power supply depends on power loading management and abnormal behavior diagnosis to construct suitable supply of power system. One of the aims of this thesis is to construct a diagnosing and analyzing system. We collect the data of each motor’s operating condition through assigned scheduling until rescheduling is triggered. To establish an abnormal behavior model that can be applied at an appropriate time to conduct basic rescheduling in accordance with dispatching rules and facilitate better performance, the collected data are classified by two methods: classification tree and self-organizing feature maps (SOM). The second aim is to construct a predictive maintenance system that uses fuzzy inference to predict the of battery power supply level using the collected information of residual power and temperature, and considering the power loss of using cycle and rising temperature of battery. The administrator can easily observe the operating condition of operating robot and battery through the constructed generic message-passing platform (GMPP) web service. Once the diagnosing and analyzing system discovers any abnormal behavior or once the predictive maintenance system detects low level of battery supply, GMPP will send active warning notifications to the engineers to conduct management and repair of the robot’s motors and battery. Finally, we discuss the combination of batteries in a single package considering the limits of real application to make the functions of optimized package meet the purpose of reaching the maximum duration of usage and power.