In this paper, we introduce a new approach to fault detection for robot manipulators. The technique, which is based on the isolation of fault signatures via filtered torque prediction error estimates, does not require measurements or estimates of manipulator acceleration as is the case with some previously suggested methods. The method is formally demonstrated to be robust under uncertainty in ...

This paper is concerned with the robust detection of abnormalities occurring in complex operations such as those found in industrial processes. A methodology for detection of process abnormalities is presented which uses only uncertain dynamic process models derived from historical operational data and little !a priori fault information. The emphasis of the methodology is to provide a robust, o...

Low fault currents, associated with Vegetation High Impedance Fault (VeHIF) events, are often challenging to clear by relying on traditional overcurrent based protection schemes. Frequency (LF) harmonics have readily been used in the literature as potential detection features. (HF) signatures also generated during VeHIFs events. These generally underrepresented, potentially for reducing computa...

An interturn short-circuit fault in the stator winding of an electric machine, denoted as turn fault, has been recognized one most severe faults permanent magnet machines, which require a swift and reliable detection, order to implement appropriate mitigations. The asymmetry brought by is widely used for detection. However, similar features also emerge less high-resistance connection (HRC) may ...

This paper presents a novel fault detection and isolation (FDI) architecture applied to the lateral-directional axis of an F-14 aircraft during powered approach to landing under sensor failures. The fault-tolerant architecture employed is based on the so-called “Robust Multiple-Model Adaptive Control” (RMMAC) and hence is referred to as “RMMAC/FDI”. The results demonstrate successful stability ...

This paper focus on the problem of passive robust fault detection using nonlinear models that include parameter uncertainty. The non-linear model considered here is described by a Group Method of Data Handling Neural Network (GMDHNN). The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed checking if the measured behaviour is inside ...