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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Journal ArticleDOI
Signal anomaly identification strategy based on Bayesian inference for nuclear power machinery
TL;DR: A long short-term memory model is established to predict the time-series signals and it is demonstrated that the proposed method can issue an alarm several hours in advance and provide a fault probability, which improves the accuracy and reliability of prediction.
Proceedings ArticleDOI
A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks
TL;DR: In this article, a dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value, and a series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.
Journal ArticleDOI
An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries
TL;DR: In this article , the authors proposed a methodology for dynamic risk prediction by integrating fault tree (FT), dynamic Bayesian network (DBN) and support vector regression (SVR), which can be applied for risk early warning of lithium-ion battery thermal runaway.
Journal ArticleDOI
LM-CNN: A Cloud-Edge Collaborative Method for Adaptive Fault Diagnosis With Label Sampling Space Enlarging
TL;DR: A cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing and a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed.
Proceedings ArticleDOI
Monotonicity Induced Parameter Learning for Bayesian Networks with Limited Data
TL;DR: This paper proposes a data-dependent method to learn the parameters of BN with limited data using Spearman rank correlation coefficient (RHO) and bidirectional monotonicity constraints are introduced into R HO-PML as RHO-BPML.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
Bayesian networks and decision graphs
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Journal Article
Big data: the management revolution.
Andrew McAfee,Erik Brynjolfsson +1 more
TL;DR: Big data, the authors write, is far more powerful than the analytics of the past, and executives can measure and therefore manage more precisely than ever before, and make better predictions and smarter decisions.
Journal ArticleDOI
A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
Journal ArticleDOI
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.