Critical cases of a CNC drive system-fault diagnosis via a novel architecture

Abstract

The application of a novel fuzzy-neural architecture to diagnose faults in critical cases of a CNC X-axis drive system is described. The proposed architecture utilizes the concepts of fuzzy clustering, fuzzy decision making and RBF neural networks to create a suitable model based fault detection and isolation (FDI) structure. In the present application, the authors emphasize the faults due only to the nonlinear components and the components that have a more significant effect on overall accuracy of the drive system. On 100 tests on the system, i.e. the appropriate model, the diagnostic system allocated fault location and fault size 100 per cent correctly.