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
TL;DR: The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones.
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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
Ontology-driven generation of Bayesian diagnostic models for assembly systems
Mohamed S. Sayed,Niels Lohse +1 more
TL;DR: This work proposes integrating failure mode and effect analysis (FMEA) information into design ontologies and using the resulting integral models for the generation of Bayesian diagnostic networks and demonstrates the validity of the approach with an industrial case study.
Journal ArticleDOI
Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure
A. Chan,Ken R. McNaught +1 more
TL;DR: This paper employs Bayesian networks to model the domain knowledge that comprises the operations of the System Under Test, Automated Test Equipment (ATE), and the diagnostic skill of experienced engineers, in an attempt to enhance the efficiency and reliability of the diagnostic process.
Journal ArticleDOI
Developing new machine learning ensembles for quality spine diagnosis
TL;DR: New hybrid machine learning ensembles for improving the performance of a computer aided diagnosis system integrated with multimethod assessment process and statistical process control, used for the spine diagnosis based on noninvasive panoramic radiographs are adduced.
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An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds
Tina Birk Jensen,Anders Kristensen,Nils Toft,Niels Peter Baadsgaard,Søren Dinesen Østergaard,Hans Houe +5 more
TL;DR: A Bayesian network was constructed that can estimate risk indexes for three cause-categories of leg disorders in a finisher herd that enabled the value of performing systematic collection of additional information when identifying causes of leg Disorders at herd level.
Journal ArticleDOI
An approach for developing diagnostic Bayesian network based on operation procedures
TL;DR: A novel approach of developing the Bayesian network for fault diagnosis based on operation procedures is presented and the presented approach is applied to hydraulic control system of subsea blowout preventer (BOP).