A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots

- 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.

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.