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
Takagi-Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm
Changsong Li,Gu Xiong,Xiao Fan Fu,Ali Wagdy Mohamed,Xufeng Yuan,Mohammed Azmi Al-Betar,Ponnuthurai Nagaratnam Suganthan +6 more
TL;DR: In this paper , an improved fault section diagnosis (FSD) method is proposed by using Takagi-Sugeno fuzzy neural networks (T-S FNN), which combines an adaptive knowledge ratio and a genetic learning strategy to balance population diversity and convergence speed to boost the optimization ability.
Journal ArticleDOI
Structure function learning of hierarchical multi-state systems with incomplete observation sequences
TL;DR: A dynamic Bayesian network (DBN) model is put forth to leverage incomplete observation sequences of hierarchical multi-state systems for structure function learning and the results demonstrate that the structure function of a hierarchicalmulti-state system can be accurately learned despite the incompleteness of observation sequences.
Journal ArticleDOI
High-Performance Fault Classification Based on Feature Importance Ranking-XgBoost Approach with Feature Selection of Redundant Sensor Data
TL;DR: An FIR-XgBoost method, the core of which is to retain important features and to reduce redundancy of sensor data, achieves the best fault classification performance among the compared algorithms and can be implemented in specific industrial processes.
Journal ArticleDOI
Fusion-Learning of Bayesian Network Models for Fault Diagnostics.
TL;DR: In this article, the authors propose a method for fusion-learning of BNs: for quantitative data obtained from sensors, metrology data and qualitative data from maintenance logs, corrective and preventive action reports, and then follow by fusing these two BNs.
Journal ArticleDOI
Parallel tensor factorization for relational learning
TL;DR: This article focuses on devising a parallel version for RESCAL, one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity.
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.