Nonlinear Fault Detection for Hydraulic Systems

One of the most important areas in the robotics industry is the development of robots capable of working in hazardous environments. As humans cannot safely or cheaply work in these environments, providing a high level of robotic functionality is important. Our work in this area focuses on a fault detection method known as analytical redundancy, or AR. In this paper we discuss the application to a hydraulic servovalve system of our novel rigorous nonlinear AR technique. AR is a model-based state-space technique that is theoretically guaranteed to derive the maximum number of independent tests of the consistency of sensor data with the system model and past control inputs. Conventional linear AR is only valid for linear sampled data systems. However, our new nonlinear AR (NLAR) technique maintains traditional linear AR’s mathematical guarantee to generate the maximum possible number of independent tests in the nonlinear domain. Thus NLAR allows us to gain the benefits of AR testing for nonlinear systems with both continuous and sampled data

Expert System Framework for Fault Detection and Fault Tolerance in Robotics

Fault tolerance is of increasing importance for modern robots. The ability to detect and tolerate failures enables robots to effectively cope with internal failures and continue performing assigned tasks without the need for immediate human intervention. To monitor fault tolerance actions performed by lower level routines and to provide higher level information about a robot's recovery capabilities, we present an expert system and critic which together form a novel and intelligent fault tolerance framework integrating fault detection and tolerance routines with dynamic fault tree analysis. A higher level, operating system inspired critic layer provides a buffer between robot fault tolerant operations and the user. The expert system gives the framework the modularity and flexibility to quickly convert between a variety of robot structures and tasks. It also provides a standard interface to the fault detection and tolerance software and a more intelligent means of monitoring the progress of failure and recovery throughout the robot system. The expert system further allows for prioritization of tasks so that the components essential to fault detection and tolerance within a system and detail the interconnection between failures in the system. The trees are also used quantitatively to provide a dynamic estimate of the probability of failure of the entire system or various subsystems.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

Evaluating the reliability of prototype degradable systems

The technique introduced in this paper is a new technique for analyzing fault tolerant designs under considerable uncertainty, such as seen in unique or few-of-a-kind devices in poorly known environments or pre-prototype design analyses. This technique is able to provide useful information while maintaining the uncertainty inherent in the original specifications. The technique introduced here is a logical extension of the underlying concepts of fuzzy sets and Markov models. Although originally developed for robotic systems, the technique is more broadly applicable. This paper develops fuzzy Markov modeling and uses it to analyze a specific robot designed for hazardous waste removal and specific types of electronic systems.National Science FoundationSandia National Laborator

Robot Fault Detection and Fault Tolerance: A Survey

Fault tolerance is increasingly important for robots, especially those in remote or hazardous environments. Robots need the ability to effectively detect and tolerate internal failures in order to continue performing their tasks without the need for immediate human intervention. Recently, there has been a surge of interest in robot fault tolerance, and the subject has been investigated from a number of points of view. Ongoing research performs off-line and on-line failure analyses of robotic systems, develops fault-tolerant control environments, and derives fault detection and error recovery techniques using hardware, kinematic, or functional redundancy. This paper presents a summary of the current, limited, state-of-the-art in fault-tolerant robotics and offers some future possibilities for the field.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi