Quantitative System Risk Assessment From Incomplete Data With Belief Networks and Pairwise Comparison Elicitation. [PDF]
De Persis C +4 more
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Machine learning based fault classification for improved induction motor performance. [PDF]
Khan ZA +5 more
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A Framework for Digital Technologies Application in Soft Sensing Monitoring to Recombinant Nanobody Production in <i>Escherichia coli</i>: From Model Explanation to the Study of Potential Scenario. [PDF]
Acosta-Pavas JC +5 more
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Explainable AI for post-hoc and pseudo-post-hoc predictive maintenance of governor valve actuators. [PDF]
Tang J, Liu Y, Huang X, Jiang Z, Wu F.
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Hierarchical Fuzzy Adaptive Observer-Based Fault-Tolerant Consensus Tracking for High-Order Nonlinear Multi-Agent Systems Under Actuator and Sensor Faults. [PDF]
Zhao L, Chen S.
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AUTOMATIC FAULT-TREE GENERATION
IFAC Proceedings Volumes, 1994This paper presents an expert system approach to off-line generation and optimisation of fault-trees for use in on-line fault diagnosis systems, incorporating the knowledge and experience of manufacturers and users. The size of the problem is such that explicit formulation of the fault-tree is very complicated.
B.D. Netten, R.A. Vingerhoeds
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From Fault-Tree To Fault-Identification
IEEE Transactions on Reliability, 1983Summary: A practical way is given of identifying actual faults, by using a fault tree's complete system of minimal cutsets. For instance, for a fault tree where 20 cutsets are considered with 30 possible primal events, any of them can be found in at most three steps by the proposed FID-algorithm.
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The purpose of this study was to use fault tree analysis to evaluate the adequacy of quality reporting programs in identifying root causes of postoperative bloodstream infection (BSI). A systematic review of the literature was used to construct a fault tree to evaluate 3 postoperative BSI reporting programs: National Surgical Quality Improvement ...
Lisa M, McElroy +6 more
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