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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Proceedings ArticleDOI
Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary Process
TL;DR: In this article , a sparse causal analysis model with time-varying parameters is proposed to characterize predictive relationships between variables and avoid repeated modeling, and an update strategy that constrains the gradient information to guarantee sparsity.
Book ChapterDOI
Background
TL;DR: Fault detection and diagnosis (FDD) as discussed by the authors is a scientific field emerged in the middle of the twentieth century with the rapid development of science and data technology, it manifests itself as the accurate sensing of abnormalities in the manufacturing process, or the health monitoring of equipment, sites, or machinery in a specific operating site.
Proceedings ArticleDOI
A Fault Diagnosis Method of Power Networks Including Information Attacks
TL;DR: In this article , the authors proposed a power grid fault diagnosis method considering the attack of fault information, which is very hidden, extremely destructive, and can perfectly evade existing fault diagnosis methods.
Journal ArticleDOI
Analysis and Simulation of Multimedia English Auxiliary Handle Based on Decision Tree Algorithm
TL;DR: The experiment shows that the improved decision tree algorithm can not only judge whether there is a problem with the language sense of the candidate’s English composition, but also provide a basis for the overall evaluation of the composition.
Proceedings ArticleDOI
Fault diagnosis model of deep belief networks based on adaptive genetic algorithm optimization
TL;DR: The AGADBN fault diagnosis model proposed in this paper is verified by the MNIST data set and the convergence speed and recognition accuracy are significantly improved, which is of positive significance for improving the application of deep neural network in fault diagnosis field.
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.