KNOWLEDGE BASED INCIPIENT FAULT DIAGNOSIS OF INDUSTRIAL ROBOTS

A computer assisted incipient fault diagnosis system (CAFD) for industrial robots is introduced, with the objective to detect and diagnose faults in the mechanical part of the devices at a relatively early stage to prevent subsequent damages. For this purpose a suitable combination of analytical and heuristic tools was developed. First the analytical symptom generation procedure is described. It is comprised of the detailed mathematical modeling of the robot's different axes. The parameters of the models directly represent characteristic physical quantities (process coefficients) being identified by specific continuous-time parameter estimation algorithms. The estimated quantities then undergo a statistical evaluation. Deviation of coefficients from nominal values are considered as symptoms. The subsequent heuristic evaluation processes these symptoms based on specific fault-symptom–trees and knowledge of fault statistics and process history using a specific inference mechanism. The developed diagnosis system was realized on a PC with process interface. Experimental results obtained from an industrial robot show the feasibility and efficiency of the devised method. Practical issues concerning the applicability of the incipient fault diagnosis system are finally discussed.