Automatica (Journal of IFAC)
Robot Reliability and Safety
Robust Fault Detection of a Robotic Manipulator
International Journal of Robotics Research
Fault-tolerant robot manipulators based on output-feedback H∞ controllers
Robotics and Autonomous Systems
A model-based approach to robot fault diagnosis
Knowledge-Based Systems
Fault identification for robot manipulators
IEEE Transactions on Robotics
Paper: A survey of design methods for failure detection in dynamic systems
Automatica (Journal of IFAC)
Process fault detection based on modeling and estimation methods-A survey
Automatica (Journal of IFAC)
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Modern robotic systems perform elaborate tasks in complicated environments and have close interactions with humans. Therefore fault detection and isolation (FDI) schemes must be carefully designed and implemented on robotic systems in order to guarantee safe and reliable operations. In this paper, we propose a hierarchical multiple-model FDI (HMM-FDI) scheme to detect and isolate actuator faults of robot manipulators. The proposed algorithm performs FDI in stages and refines the associated model set at each stage. Consequently only a small number of models are required to detect and isolate various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults. In addition, the computational load is alleviated due to the reduced-sized model set. The relation between the fault detection stage of the HMM-FDI scheme and the likelihood ratio test is explicitly revealed and theoretical upper bounds of the false alarm and missed detection probabilities are evaluated. Then we conduct experiments to demonstrate the ability of the HMM-FDI scheme in successful and immediate detection and isolation of actuator faults.