Event
SHAPE: An Energy-Based Fault Detection Framework Without Dedicated Observers
1 reports · 1 sources
Johnny Garcia and co-authors propose SHAPE, an energy-based fault detection and isolation framework that combines port-Hamiltonian variables with differential flatness to generate nominal power references from planned motion, constructing mechanical port power residuals without the need for dedicated dynamic observers. In simulations of a six-degree-of-freedom Kinova robotic arm under model predictive control (with fixed model mismatch and a specified single joint fault), across five training seeds and 3000 held-out tests, SHAPE-CWT achieved an average accuracy of 84.69%, torque residuals 81.67%, and the generalized momentum observer 97.17%. The authors note that severity excitation profiles and offline velocity perturbations limit the diagnostic capability of the method.
Report timeline
- SHAPE: An Energy-Based Fault Detection and Isolation Framework for Flat Port-Hamiltonian Mechanical Systems
The paper proposes an energy-based fault diagnosis framework, SHAPE, which combines port-Hamiltonian variables and differential flatness to generate nominal power references from planned motion, constructing mechanical port-power residuals without a dedicated dynamic observer. In simulations of a six-degree-of-freedom Kinova manipulator under model predictive control, SHAPE-CWT achieved an average accuracy of 84.69% on 3000 held-out tests, compared with 81.67% for torque residuals and 97.17% for a generalized momentum observer. The authors note that severity-excitation profiles and offline velocity perturbations limit the method's diagnostic capability.
arXiv cs.RO ·