Diagnostic and predictive maintenance systems for abnormal behavior of power scheduling loading and its application to robotics systems

Through development of intelligent robots, batteries supplied to robots discharge too quickly. 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. This paper aims to construct a diagnosing and analyzing system for the robotic power management. We collect the data of each motor´s operating condition through assigned scheduling until rescheduling is triggered. The collected data are classified by two methods: CART and SOM. The second aim of the paper is to construct a predictive maintenance system that uses fuzzy inference to predict the 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 GMPP service to conduct management and repair of the robot´s motors and battery. The experiments justify the proposed methodology.