Journal ArticleDOI
Bayesian Networks in Fault Diagnosis
Baoping Cai,Huang Lei,Min Xie +2 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
An RBMs-BN method to RUL prediction of traction converter of CRH2 trains
TL;DR: Improvements on BN modeling are made in this paper, to handle the closed-loop control structure of engineering systems, and to improve prediction performance with reduced complexity.
Journal ArticleDOI
Coupling data-driven and model-based methods to improve fault diagnosis
M. Amine Atoui,Achraf Cohen +1 more
TL;DR: In this paper, a hybrid method for diagnosing single and multiple simultaneous faults, while considering unknown operating conditions, is proposed, which consists of a Bayesian classifier combining statistical decisions and fault signature matrix.
Journal ArticleDOI
Active Fault Identification by Optimization of Test Designs
TL;DR: A comprehensive model-based FDI framework is proposed to improve fault identifiability and reduce false alarms during maintenance testing and is applied on two case studies that compare the identifiable of faults at nominal and optimal system test conditions.
Journal ArticleDOI
Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions.
TL;DR: This method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition(SVD) and is employed to diagnose the faults under the variable conditions.
Journal ArticleDOI
Analysis of network cascading failure based on the cluster aggregation in cyber-physical systems
Chao Zhang,Xin Xu,Hongyan Dui +2 more
TL;DR: A cascading failure model that considers different types of nodes and edges and their contribution to the network is developed and analyzed based on cluster aggregation in cyber-physical systems and can detect the most important nodes, edges, and critical failure paths.
References
More filters
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.