59 research outputs found
Anomaly Detection Method of Aircraft System using Multivariate Time Series Clustering and Classification Techniques
No full textA data-driven fault tree for a time causality analysis in an aging system
No full textThis paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to interpret the effect of aging on the fault causality structure over time. At periodic intervals, the model captures the cause–effect relations in the form of interpretable logic trees, then represents them in one fault tree model that reflects the changes in the fault causality structure over time due to the system aging. The proposed model provides a prognosis of the probability for fault occurrence using a set of extracted causality rules that combine the discovered root causes over time in a bottom-up manner. The well-known NASA turbofan engine dataset is used as an illustrative example of the proposed methodology
Using Artificial Intelligence for Block Maintenance of Pavement Segments with Similar Degradation
No full textA Data-Driven Fault Tree for a Time Causality Analysis in an Aging System
No full textThis paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to interpret the effect of aging on the fault causality structure over time. At periodic intervals, the model captures the cause–effect relations in the form of interpretable logic trees, then represents them in one fault tree model that reflects the changes in the fault causality structure over time due to the system aging. The proposed model provides a prognosis of the probability for fault occurrence using a set of extracted causality rules that combine the discovered root causes over time in a bottom-up manner. The well-known NASA turbofan engine dataset is used as an illustrative example of the proposed methodology
Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis
No full text
Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis
No full textMulti-output regression using polygon generation and conditional generative adversarial networks
No full textArtificial Intelligence-Based Survival Analysis For Industrial Equipment Performance Management
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