Fault tolerance is increasingly important in industrial robots. The ability to detect and tolerate failures allows robots to effectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention. To tolerate hardware failures, a set of fault tolerance algorithms are written for each component. These processes are responsible for detecting faults in their respective component and minimising the impact of the failure on the robot's performance. This work presents new intelligent neuro-fuzzy fault detection algorithms, which detect failures in robot components using analytical redundancy relations. An intelligent fault tolerance framework is proposed in which a fault component database or rule base and the detection algorithms work together to detect and tolerate sensor or motor failures in a robot system. Motor faults as well as sensor faults are considered. The Scorbot ER 5u plus model was simulated in robotics toolbox for MATLAB using the neuro-fuzzy algorithms.
- Inderscience Publishers
- A neuro-fuzzy-based fault detection and fault tolerance methods ...
The recipe for the successful sales of environmental sensors
“One accurate measurement is worth a thousand expert opinions”. This quote from Grace Hopper, the famous American computer scientist, provides a good insight of my passion for the measurement industry. There’s no wonder why I decided to meet the challenge of working at Lufft again: The company, which I already worked for from 2008 until 2012, gave me a real taste for this business. In this increasingly interconnected world, sensors are in use at the most critical point of the information...
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