Journal ArticleDOI
Bayesian Networks in Fault Diagnosis
Baoping Cai,Huang Lei,Min Xie +2 more
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TLDR
Current gaps and challenges on use of BNs in fault diagnosis in the last decades with focus on engineering systems are explored and several directions for future research are explored.Abstract:
Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.read more
Citations
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Improving the accuracy of medical diagnosis with causal machine learning.
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Availability-Based Engineering Resilience Metric and Its Corresponding Evaluation Methodology
TL;DR: The resilience value of an engineering system can be predicted using the proposed methodology, which provides implementation guidance for engineering planning, design, operation, construction, and management.
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Application of Bayesian Networks in Reliability Evaluation
TL;DR: A bibliographic review of BNs that have been proposed for reliability evaluation in the last decades is presented, and a few upcoming research directions that are of interest to reliability researchers are identified.
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Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
Aniekan Essien,Cinzia Giannetti +1 more
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References
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Journal ArticleDOI
Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach
Zhengdao Zhang,Feilong Dong +1 more
TL;DR: A multi-time-slice dynamic Bayesian network with a mixture of the Gaussian output and MT-DBNMG based data-driven fault identification method is proposed to handle the missing data samples and the non-Gaussian process data.
Journal ArticleDOI
Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences
Fei Qi,Biao Huang +1 more
TL;DR: The important evidence dependency problem is solved by a data-driven Bayesian approach with consideration of evidence transition probability and the applications in a simulated distillation column and pilot scale process are presented.
Journal ArticleDOI
Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network
TL;DR: A probabilistic fault diagnosis approach of SIS is presented, a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN) that will generate a diagnosis map that will be useful to guide repair actions.
Journal ArticleDOI
Approximate inference for medical diagnosis
Wim Wiegerinck,Hilbert J. Kappen,E. W. M. T. ter Braak,W. J. P. P. ter Burg,M.J. Nijman,Y. L.O. Neijt +5 more
TL;DR: This paper sketches how variational techniques with tractable structures can be used in a typical model for medical diagnosis and describes the approach to develop the Bayesian network for the DSS and shows some preliminary results.
Journal ArticleDOI
Automatic diagnosis of mobile communication networks under imprecise parameters
TL;DR: A model based on discrete bayesian networks (BNs) is proposed for diagnosis of radio access networks of cellular systems, intended to decrease the sensitivity of diagnosis accuracy to imprecision in the definition of the model parameters.