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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Availability-Based Engineering Resilience Metric and Its Corresponding Evaluation Methodology
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Application of Bayesian Networks in Reliability Evaluation
TL;DR: A bibliographic review of BNs that have been proposed for reliability evaluation in the last decades is presented, and a few upcoming research directions that are of interest to reliability researchers are identified.
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Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
Aniekan Essien,Cinzia Giannetti +1 more
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References
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Journal ArticleDOI
Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis
TL;DR: In this article, a fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed to improve the anti-noise ability and detect incipient fault.
Journal ArticleDOI
A dynamic Bayesian network based approach to safety decision support in tunnel construction
Xianguo Wu,Liu Huitao,Limao Zhang,Limao Zhang,Miroslaw J. Skibniewski,Miroslaw J. Skibniewski,Qianli Deng,Jiaying Teng +7 more
TL;DR: This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time, to overcome deficiencies of traditional fault analysis methods.
Journal ArticleDOI
Automated Diagnosis for UMTS Networks Using Bayesian Network Approach
TL;DR: Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.
OtherDOI
Bayesian Networks in Reliability
TL;DR: The properties of the modeling framework that are of highest importance for reliability practitioners are discussed.
Journal ArticleDOI
Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors
TL;DR: In this paper, a diagnostic Bayesian networks (DBNs)-based method is proposed to diagnose 28 faults, which cover most of common faults in air handling units (AHUs), and four DBNs are developed to diagnose faults in fans, dampers, ducts, filters and sensors.