Journal ArticleDOI
Recent advances in sensor fault diagnosis: a review
TLDR
A comprehensive review on the contemporary fault diagnosis techniques is provided and helps researchers and practitioners to understand the current state of the art development in this emerging field.Abstract:
If this is to be REF-eligible it needs a full text file attached, this cannot be final published pdf but could be version after review but before publisher formatting applied researcher confirmed item not refabable so happy to process without file 13/5/20read more
Citations
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Journal ArticleDOI
CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction
TL;DR: In this paper , a deep learning framework for linear systems with time-invariant parameters that identifies the presence and type of fault in sensor data, location of the faulty sensor and subsequently reconstructs the correct sensor data for fault detection, fault classification, and reconstruction is introduced.
Journal ArticleDOI
A novel decision support system for managing predictive maintenance strategies based on machine learning approaches
TL;DR: In this article, a decision support system based on decision trees (DTs) is proposed to guide the decision-making process of predictive maintenance (PdM) implementation, considering context-aware information, quality and maturity of collected data, severity, occurrence and detectability of potential failures (identified through FMECA analysis) and direct and indirect maintenance costs.
Journal ArticleDOI
A novel decision support system for managing predictive maintenance strategies based on machine learning approaches
TL;DR: In this paper , a decision support system based on decision trees (DTs) is proposed to guide the decision-making process of predictive maintenance (PdM) implementation, considering context-aware information, quality and maturity of collected data, severity, occurrence and detectability of potential failures (identified through FMECA analysis) and direct and indirect maintenance costs.
Journal ArticleDOI
Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods
Xingang Zhao,Junyung Kim,Kyle Warns,Xinyan Wang,Pradeep Ramuhalli,Sacit M. Cetiner,Hyun Gook Kang,Michael Golay +7 more
TL;DR: In this article, the authors provide an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
A Survey of Outlier Detection Methodologies
Victoria J. Hodge,Jim Austin +1 more
TL;DR: A survey of contemporary techniques for outlier detection is introduced and their respective motivations are identified and distinguish their advantages and disadvantages in a comparative review.
Journal ArticleDOI
A Survey of Fault Detection, Isolation, and Reconfiguration Methods
TL;DR: A survey of the various model-based FDIR methods developed in the last decade is presented, and various techniques of implementing reconfigurable control strategy in response to faults are discussed.
Proceedings ArticleDOI
An analysis of a large scale habitat monitoring application
TL;DR: An analysis of data from a second generation sensor networks deployed during the summer and autumn of 2003 sheds light on a number of design issues from network deployment, through selection of power sources to optimizations of routing decisions.
Journal ArticleDOI
A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems
Alan S. Willsky,Harold L. Jones +1 more
TL;DR: In this article, the authors consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times and devise an adaptive filtering system for the detection and estimation of the jumps.