Abstract
Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.
Highlights
Electric motors are an essential part of most manufacturing robotics
A state descriptor was proposed to describe general motor signals, and it is robust to noises
The semantic stages of a robotic system were concluded with a few healthy running cycles as combinations of state descriptors
Summary
Electric motors are an essential part of most manufacturing robotics. There are many kinds of motors that are capable of driving the system to accomplish cyclic, linear, or more compound actions. Robotics is designed to do some dedicated tasks by controlling a group of motors with servo systems or computers. Modern servo motors usually come with various sensors, which help with the analysis of the whole system. The servo controller is responsible for precisely controling the speed and position of motors. It receives control signals and transforms them into a torque output to drive the motor. The mechanism of the servo controller involves a closed-loop system, which synchronously reads position and speed sensors of the motor to adjust the torque output continuously. Most servo motors would share sensor interfaces to the user for customized control. From the recorded sensor data, it is possible to manually check out whether the robotic system is following the right routine. If there are foreign objects in the actuator, or the bearing is lacking lubricant oil, the quality of product may be affected; the duration of the system will be shortened too
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