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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Development of 3000 m Subsea Blowout Preventer Experimental Prototype
TL;DR: A subsea blowout preventer experimental prototype is developed to meet the requirement of training operators, and the prototype consists of hydraulic control system, electronic control system and small-sized blow out preventer stack.
Journal ArticleDOI
Gated Recurrent Unit-Enhanced Deep Convolutional Neural Network for Real-time Industrial Process Fault Diagnosis
TL;DR: Wang et al. as discussed by the authors proposed a novel Gated Recurrent Unit (GRU) -enhanced deep convolutional neural network (EDCNN) model for the improved fault detection and diagnosis of chemical processes.
Journal ArticleDOI
Probabilistic Analysis of Wheel Loader Failure under Rockfall Conditions Based on Bayesian Network
TL;DR: In this paper, a fault diagnosis model based on Bayesian network (BN) was proposed to diagnose the probability and path of the fault occurrence in the wheel loader during a rockfall disaster.
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Fault diagnosis for mechanical system using dynamic Bayesian network
TL;DR: A dynamic Bayesian network is established to model the dynamic degradation of components in a system under additional information by using the wear data and the parameters of the model are estimated by historical data.
Book ChapterDOI
The Research on the Framework of Machine Fault Diagnosis in Intelligent Manufacturing
TL;DR: A fault diagnosis framework based on DEMATEL and Support Vector Machine is proposed in this paper that improves the efficiency of fault diagnosis effectively and ensures the further development of intelligent manufacturing.
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.