Early failure characterization of cantilever snap assemblies using the PA-RCBHT

Failure detection and correction is essential in robust systems. In robotics, failure detection has focused on traditional parts assembly, tool breakage, and threaded fastener assembly. However, not much work has focused on sub-mode failure classification. This is an important step in order to provide accurate failure recovery. Our work implemented a novel failure characterization scheme for cantilever snap assemblies. The approach identified exemplars that characterized salient features for specific deviations from a nominal trajectory. Then, a rule based approach with statistical measures was used to identify failure and classify failure sub-modes. Failure sub-mode classification was evaluated by using a reliability measure. Our work classified failure deviations with 88% accuracy. Varying success was experienced in correlating failure deviation modes. Cases with only 1-deviation had 86% accuracy, cases with 2-deviations had 67% accuracy, and cases with 3 deviations had 55% accuracy. Our work is an important step in failure characterization of complex geometrical parts and serves as a stepping stone to enact failure recovery.