Topic
Fault indicator
About: Fault indicator is a research topic. Over the lifetime, 10057 publications have been published within this topic receiving 143482 citations. The topic is also known as: FCI & power line fault indicator.
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Papers
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TL;DR: The inference speed and accuracy of the method proposed are much better than those of the one proposed earlier which does not use time information, and the inference speed is also fast enough for real-time use.
41 citations
29 Sep 2014
TL;DR: In this article, the authors describe the detection and isolation (diagnosis) of faults (major equipment and sensor/actuator malfunctions) in engineering systems, which do not rely on any mathematical model of the system.
Abstract: The article describes the detection and isolation (diagnosis) of faults (major equipment and sensor/actuator malfunctions) in engineering systems. The simpler, and less powerful methods do not rely on any mathematical model of the system; these include limit checking, special and multiple sensors, frequency analysis, and fault-tree analysis. More advanced methods use mathematical models obtained from first principles or from experimental data. Such methods include on-line parameter estimation, consistency checking, and principal component analysis. Two examples, one related to a simple electrical circuit and the other to a car-engine subsystem, demonstrate the use of some of the methods.
Keywords:
fault detection;
fault diagnosis;
limit checking;
frequency analysis;
fault trees;
parameter estimation;
consistency relations;
principal component analysis
41 citations
TL;DR: In this article, a model-based fault detection and diagnosis system for the Space Shuttle main engine is developed for the space shuttle main engine using a discrete time, quasilinear state-space representation.
Abstract: A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.
41 citations
TL;DR: In this article, the authors re-examined the fault detectability and isolability of the residual signal of an inverted pendulum system with possible sensor, actuator and component faults.
41 citations
12 Oct 2003
TL;DR: In this paper, an advanced technique for detecting incipient inter-laminar insulation failure in stator cores of large electric machines is proposed, which is an offline method based on low flux stator core excitation that employs a new probe.
Abstract: An advanced technique for detecting incipient inter-laminar insulation failure in stator cores of large electric machines is proposed in this paper. The proposed scheme is an offline method based on low flux stator core excitation that employs a new probe. An iron-core probe is placed in the wedge depression area of the stator and scanned axially to detect the variation in the measured flux due to a fault. Two fault indicators that provide clear indication on fault severity and location are proposed in this paper. A 120-MW ac machine stator was tested under a number of intentionally inserted fault conditions using portable field equipment recently developed. The experimental results show that the fault existence, location, and severity can be determined reliably using the proposed scheme. It is also shown that the fault detection sensitivity and reliability, inspection speed, and user convenience are significantly enhanced.
41 citations