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Survey of modern Fault Diagnosis methods in networks [article]

Zi Jian Yang, Yong Wang
2017 arXiv   pre-print
This paper reviews the history of fault diagnosis in networks and discusses the main methods in information gathering section, information analyzing section and diagnosing and revolving section of fault  ...  With the advent of modern computer networks, fault diagnosis has been a focus of research activity.  ...  Fault diagnosis based on Bayesian Networks (BN) Bayesian networks ( also Belief networks or directed acyclic graphical model) is a probabilistic graphical model that represents a set of random variables  ... 
arXiv:1702.01510v1 fatcat:el7f6qd3erabvmsk2qqatld4gu

Deep Learning-Based Machinery Fault Diagnostics

Hongtian Chen, Kai Zhong, Guangtao Ran, Chao Cheng
2022 Machines  
In recent years, deep learning has shown its unique potential and advantages in feature extraction and pattern recognition [...]  ...  First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output was constructed to adapt to the variable  ...  In [15] , a process monitoring method based on the dynamic autoregressive latent variable model was proposed in this paper.  ... 
doi:10.3390/machines10080690 fatcat:bes45ftp2zdtfparxwnbfpo2s4

Fault classification and diagnostic system for unmanned aerial vehicle electrical networks based on hidden Markov models

Rory Telford, Stuart Galloway
2015 IET Electrical Systems in Transportation  
2015) Fault classification and diagnostic system for UAV electrical networks based on hidden Markov models. IET Electrical Systems in Transportation. ISSN 2042-9738 , http://dx.  ...  This study outlines the development of a novel UAV EPS fault classification and diagnostic (FCD) system based on hidden Markov models (HMM) that will assist and improve EPS health management and control  ...  Acknowledgments The authors would like to acknowledge the funding and support offered by Airbus Group and the Engineering and Physical Science Research Council.  ... 
doi:10.1049/iet-est.2014.0042 fatcat:g6tvl5jrlff3plvb7imyduz4lq

Deep Infinite Mixture Models for Fault Discovery in GPON-FTTH Networks

Amine Echraibi, Joachim Flocon-Cholet, Stephane Gosselin, Sandrine Vaton
2021 IEEE Access  
Another model widely used for communication network management is Bayesian Networks [13] , [14] , [15] , [16] , [17] .  ...  In the latter methodology, a directed acyclic graph, called a Bayesian graph, is built to model the network topology and depen-dencies between network features: each node represents an observed variable  ...  The research group aims to design, describe, manage, secure and control various aspects of operator communication networks, and relies on stochastic models and algorithms to capture the high variability  ... 
doi:10.1109/access.2021.3091328 fatcat:uasrv3b62zffbej5637po6ybla

2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25

2014 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Feb. 2014 356-369 The Group Latent Variable Approach to Probit Binary Classifications.  ...  . 2014 1538-1552 Yang, J., see 1433-1446 Yeun, Y.S., and Kim, N., The Group Latent Variable Approach to Probit Binary Classifications; TNNLS Jul. 2014 1277-1286 Yin, J., see Liu, X., TNNLS Jun. 2014  ...  The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014  ... 
doi:10.1109/tnnls.2015.2396731 fatcat:ztnfcozrejhhfdwg7t2f5xlype

Table of Contents

2022 IEEE Transactions on Cybernetics  
Peng 1588 A Dynamic Multiobjective Evolutionary Algorithm Based on Decision Variable Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Ye 1527 Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcyb.2022.3156005 fatcat:ydzt3xmes5cudi3qtowj5molzm

Latent Variable Models in the Era of Industrial Big Data: Extension and Beyond [article]

Xiangyin Kong, Xiaoyu Jiang, Bingxin Zhang, Jinsong Yuan, Zhiqiang Ge
2022 arXiv   pre-print
Among various data-driven methods, latent variable models (LVMs) and their counterparts account for a major share and play a vital role in many industrial modeling areas.  ...  Neural networks-based DLVMs have sufficient model capacity to achieve satisfactory performance in complex scenarios, but it comes at sacrifices in model interpretability and efficiency.  ...  Finally, the dynamic information is introduced to construct a dynamic modeling framework for fault classification. Deng et al.  ... 
arXiv:2208.10847v1 fatcat:povugv6b5zhlda6cpfwcx4ap2e

A Review on Fault Detection and Process Diagnostics in Industrial Processes

You-Jin Park, Shu-Kai S. Fan, Chia-Yu Hsu
2020 Processes  
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against  ...  However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics.  ...  In this study, they discussed the use of a latent variable modeling approach and its extensions for fault detection.  ... 
doi:10.3390/pr8091123 fatcat:hpcof76vijg7rjgwwbhdwexeg4

Progress of Process Monitoring for the Multi-Mode Process: A Review

Jie Ma, Jinkai Zhang
2022 Applied Sciences  
A complete classification framework for multi-mode process monitoring methods is produced, covering steady-state modes and transitional processes.  ...  After introducing basic concepts and describing research outcomes obtained for monitoring methods, prospects for multi-mode process monitoring technology are discussed.  ...  The loading matrix and latent variable matrix were obtained by the dynamic-inner PCA iterative data matrix to construct a vector auto-regressive (VAR) model and extract dynamic features.  ... 
doi:10.3390/app12147207 fatcat:3hhzbngqnrc4xmnsii4ujl54gu

Bayesian Networks in Educational Assessment

Michael J. Culbertson
2015 Applied Psychological Measurement  
Bayesian networks (BN) provide a convenient and intuitive framework for specifying complex joint probability distributions and are thus well suited for modeling content domains of educational assessments  ...  BN have been used extensively in the artificial intelligence community as student models for intelligent tutoring systems (ITS) but have received less attention among psychometricians.  ...  SM = student model. Figure 5 . 5 Simple dynamic Bayesian network with two time points.  ... 
doi:10.1177/0146621615590401 pmid:29881033 pmcid:PMC5978531 fatcat:lm6yfxf7hzfwtiz5usksi5qvma

A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

Yujie Yu, Chunguang Zhang, Yingjie Li, Yuangang Li
2020 IEEE Access  
A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed [35] .  ...  A hybrid physics-model-based and datadriven remaining useful life (RUL) estimation methodology of structure systems by using dynamic Bayesian networks (DBNs) is proposed [37] .  ... 
doi:10.1109/access.2020.3025956 fatcat:o7bno6xeojcx3fyr55xsjiyq2q

From Auto-encoders to Capsule Networks: A Survey

Omaima El Alaoui-Elfels, Taoufiq Gadi, S. Krit
2021 E3S Web of Conferences  
Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation.  ...  We compare different CapsNets series to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains.  ...  On the other hand, due to the high variability in text, CapsNetstatic-routing is not robust enough for document classification as opposed to image classification.  ... 
doi:10.1051/e3sconf/202122901048 fatcat:2kbpawcl5bfozivfx5fo6nssuq

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Martí de Castro-Cros, Manel Velasco, Cecilio Angulo
2021 Energies  
A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research  ...  This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational  ...  Acknowledgments: The authors would like to thank Davood Naderi and Edgar Bahilo from Siemens Energy, Service Oil & Gas Sweden and Stefano Rosso for his support and assistance during the elaboration of  ... 
doi:10.3390/en14248468 fatcat:kjngrifowfgo3epra4m752db7e

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models [article]

Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink
2019 arXiv   pre-print
With the increased availability of condition monitoring data and the increased complexity of explicit system physics-based models, the application of data-driven approaches for fault detection and isolation  ...  on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model.  ...  Contrarily to previews models, the latent representation z of the data x is a stochastic variable. Therefore, the encoder and the decoder networks are probabilistic.  ... 
arXiv:1908.01529v2 fatcat:ftmxwwawcvdepd77onfhqazkke

Guest Editorial: Data-Driven Management of Complex Systems Through Plant-Wide Performance Supervision

Okyay Kaynak, Steven Ding, Ahmet Palazoglu, Hao Luo
2021 IEEE Transactions on Industrial Informatics  
To achieve distributed process monitoring, distributed parallel mixture probabilistic latent variable model is proposed based on the stochastic variational inference algorithm and the parameter server  ...  The k-means clustering method was used to decompose the static and dynamic blocks into subblocks and the monitoring results in each subblock are integrated by Bayesian inference.  ... 
doi:10.1109/tii.2020.3023259 fatcat:2x44ydldbreqdcyejz5jui7q24
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