Energy Condition Perception and Big Data Analysis for Industrial Cloud Robotics

Industrial cloud robotics (ICRs), which is proposed to integrate the distributed industrial robots (IRs) resources to provide ICRs services at any place, has been attracted great attention due to the characteristics of convenient access, cheaper computing cost, more convenient network resources, etc. Meanwhile, in manufacturing industry, the energy-efficient issue, which means minimize the amount of energy resources to achieve a given output level in manufacturing process, is also gradually paid great attention by academia, industry and government. Currently, ICRs plays a crucial role in production. The implementation of energy-efficient manufacturing for ICRs will significantly decrease the energy consumption on the premise of normal production process, and also have remarkable effect on energy-saving and emission-reduction in manufacturing industry. In this context, the energy condition perception and big data analysis of ICRs are the essential procedure to achieve the aforementioned goals. A novel system architecture which mainly focuses on distributed energy condition perception and big data analysis for ICRs is built. Based on the perceptive data of ICRs related to energy consumption, a big data analysis model combined with the manufacturing status of ICRs is proposed, and the relationship between the big data and the analysis model is presented. Through the data analysis model, we can analyze the energy consumption fluctuation characteristic of ICRs operating state, count the energy consumption of the product related to different production phases, predict the health status of ICRs, as well as the trend of energy consumption associated with their operations. A case study is implemented to demonstrate the effectiveness of the proposed system and approaches.