A review on fault detection and diagnosis of industrial robots and multi-axis machines

Abstract

- Industrial Robots and Multi-axis Machines have become increasingly popular in recent years, in a diverse range of industries. These complex and expensive machines are vulnerable to a variety of problems that could put the robot or its surroundings in danger. To keep the system running, these issues must be discovered and diagnosed quickly. Although numerous related review papers have been increasing over time, none describe the techniques of fault diagnosis and isolation (FDI) for smart manufacturing and industrial robotic systems and their rotating components. This work reviews this issue and expands the discussion over the existing related reviews to cover the FDI techniques for Multi DOF robots. The study excludes the FDI techniques related to some types of autonomous robots like multi-robot systems, robot swarms, and UAVs out of our domain while including the associated components involved in industrial robot manufacturing such as gearbox, actuators, and controllers. A few previous studies discussed the current-signature data-driven approaches but either for a single motor, actuator, or one joint and not for the whole system of a robot manipulator faults. The literature outcome concluded that the existing methods can identify faults for only one or two DOFs and it is advisable to present an approach for repetitive Multi DOF robots that benefit from the existing techniques and their limitations to conducting a study on automatic FDI enhanced by a reference mathematical model of each task.

Keywords

• Faults detection and identification (FDI)
• Multi-degree-of-freedom robots
• Manipulator
• Multi-axis industrial machine
• Fault diagnosis