A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots. (February 2018)

• Record Type: Journal Article
• Authors: To, Andrew Wing Keung; Paul, Gavin; Liu, Dikai
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 49(2018)
• Page Start: 13
• Page End: 23
• Publication Date: 2018-02
• Subjects: Autonomous industrial robot -- Abrasive grit-blasting -- Real-time fault diagnosis -- Multi-modal sensing -- Field robotics; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2017.05.008
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: We have proposed an approach to real-time fault diagnosis during automatic grit-blasting operation performed by an autonomous industrial robot. Devised techniques for using multi-modal sensing data including RGB-D, audio and pressure to detect the real-time position of the grit-blasting spot and/or the state-of-blasting (no-blasting, air-blasting and grit-blasting). Conducted experiments in both a laboratory setup and in a blasting chamber during live grit-blasting to test the performance of the proposed approach. Demonstrated fault diagnosis accuracy of above 95% for experiments. Abstract: This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the blasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.