Incipient fault diagnosis for robot manipulators based on evolution of friction characteristics in transmission components

Abstract The tribological behavior can be informative about the incipient faults of robot manipulators. This study explores the evolution of friction characteristics from cold start to thermal equilibrium through a series of steady-state friction experiments. Based on these experimental observations, a friction-based fault diagnosis framework is proposed. The fault diagnosis process primarily involves defining the healthy state, decomposing friction curves and their features, and anomaly detection. Given the dependence of friction characteristics on different sources of faults, the parameters of steady-state experimental friction model are divided into two categories: one associated with contact interactions and the other related to non-contact regimes. Subsequently, confidence regions corresponding to distinguishable friction characteristics are independently constructed. These regions encapsulate the statistical description of the healthy state, characterized by mean values and the covariance of the friction characteristic parameter vectors during the unloaded state. In addition, we conduct experiments that consider the influence of applied loads on friction behavior. These experiments serve as a test set for comparison against nominal statistics. Leveraging the similarity between the effects of wear and load on friction, we introduce equivalent load thresholds to assess the severity of joint degradation. The results demonstrate the feasibility of employing confidence region views based on friction characteristic classification for fault detection and isolation.