Results 281 to 290 of about 81,076 (316)
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The detection of fault-prone programs
IEEE Transactions on Software Engineering, 1992The use of the statistical technique of discriminant analysis as a tool for the detection of fault-prone programs is explored. A principal-components procedure was employed to reduce simple multicollinear complexity metrics to uncorrelated measures on orthogonal complexity domains.
John C. Munson, Taghi M. Khoshgoftaar
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On the Detection of Terminal Stuck-Faults
IEEE Transactions on Computers, 1978Two lower bounds on the length of terminal stuck-fault tests and a technique to facilitate detection of terminal stuck-faults are given.
Jon G. Kuhl, Sudhakar M. Reddy
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Predicting fault detection effectiveness
Proceedings Fourth International Software Metrics Symposium, 2002Regression methods are used to model software fault detection effectiveness in terms of several product and testing process measures. The relative importance of these product/process measures for predicting fault detection effectiveness is assessed for a specific data set.
Joseph A. Morgan +2 more
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Information theoretic fault detection
Proceedings of the 2005, American Control Conference, 2005., 2005In this paper we propose a novel method of fault detection based on a clustering algorithm developed in the information theoretic framework. A mathematical formulation for a multi-input multi-output (MIMO) system is developed to identify the most informative signals for the fault detection using mutual information (MI) as the measure of correlation ...
Alok A. Joshi +4 more
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Proceedings of the 2003 American Control Conference, 2003., 2004
Current sensor FDIR (fault detection, isolation, & recovery) generally focuses on sensor bias and drift anomalies, which require models. However, dead sensors and excessive noise faults are more common in practice. The latter two faults are interesting in that they can be detected using only the measurements from each sensor.
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Current sensor FDIR (fault detection, isolation, & recovery) generally focuses on sensor bias and drift anomalies, which require models. However, dead sensors and excessive noise faults are more common in practice. The latter two faults are interesting in that they can be detected using only the measurements from each sensor.
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A survey on fault prediction and fault detection
AIP Conference Proceedings, 2023Megala Ranjith +3 more
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A review of data-driven fault detection and diagnostics for building HVAC systems
Applied Energy, 2023Zhelun Chen, Zheng O’Neill, Jin Wen
exaly