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Fault Diagnosis of Rotating Machinery Based on Evolutionary Convolutional Neural Network
2022
Shock and Vibration
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This paper proposes a fault diagnosis method for rotating machinery based on evolutionary convolutional neural network (ECNN). ...
The results show that the proposed method is helpful to obtain a convolutional neural network structure with better performance and achieve higher fault diagnosis accuracy. ...
[20] proposed an ensemble of evolutionary algorithms which combined with CNN for gas detection. Zhang et al. ...
doi:10.1155/2022/5031941
fatcat:lu72pezeuvcz7jvnda2uts6ple
Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM
2021
Shock and Vibration
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diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. ...
Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent ...
Acknowledgments e authors are grateful for the financial support provided by the National Natural Science Foundation of China under grant no. 51805151 and the Key Scientific Research Project of the University ...
doi:10.1155/2021/1205473
fatcat:qbbwiqshtrh4hcfeu2zn2y7xve
Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review
2018
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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, Convolutional Neural Networks and Deep Belief Networks. ...
Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. ...
Ensemble
Fault diagnosis and remaining useful life estimation of
aero-engine
Gao et al. [22]
Deep Belief Networks + Deep Quantum Inspired Neural
Network
Fault diagnosis of aircraft's fuel system ...
doi:10.1109/itsc.2018.8569502
dblp:conf/itsc/RengasamyMF18
fatcat:fdetlamp6vesngmpu7icccvqxe
A Recent Machine Learning Techniques for Failure Diagnosis of Rolling Element Bearing
2021
Hungarian Agricultural Engineering
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Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. ...
Early identification is an essential element in the diagnosis of defects that saves time and expenses and avoids dangerous conditions. ...
A balanced dataset is used to train a Convolutional Neural Network for fault detection. ...
doi:10.17676/hae.2021.39.42
fatcat:keuxnwlvnbd27gvxlt5ofv5d7a
A Survey on Fault Diagnosis of Rolling Bearings
2022
Algorithms
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First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. ...
Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. ...
neural networks, by using evolutionary computation (EC) techniques. ...
doi:10.3390/a15100347
fatcat:q6gav7gkgrccxlncp2bu7plcwy
A deep adaptive learning method for rolling bearing fault diagnosis using immunity
2019
Tsinghua Science and Technology
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convolutional neural network. ...
The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. ...
In this paper, to solve the problem of the time needed for the detection and learning of an adaptive algorithm model, the combination of a Deep Convolutional Neural Network (DCNN) and antibody immunity ...
doi:10.26599/tst.2018.9010144
fatcat:56u2u5v3wbfxjpyz7fc53skbyy
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
[article]
2016
arXiv
pre-print
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(DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). ...
Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. ...
Chen et al. proposed an ensemble of DBNs with multi-objective evolutionary optimization on decomposition algorithm (MOEA/D) for fault diagnosis with multivariate sensory data [80] . ...
arXiv:1612.07640v1
fatcat:46zurdmo2bcabo352qqppv5zki
A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment
2022
Energies
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In view of this problem, we propose a fault-diagnosis strategy with good noise immunity in this paper by integrating the two-dimensional convolutional neural network (2DCNN) with random forest (RF), which ...
More specifically, the raw 1D time-domain bearing-vibration signals are transformed into 2D grayscale images at first, which are then fed to the 2DCNN-RF model for fault diagnosis. ...
In this paper, we propose a two-dimensional convolutional neural network (2DCNN) model for offshore wind-turbine high-speed bearing-fault diagnosis under noisy environments, which is supposed to utilize ...
doi:10.3390/en15093340
fatcat:qkaibnzmsrh2pkaceqlprkzylu
Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis
2017
Chinese Journal of Mechanical Engineering
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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. ...
The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. ...
Guo, et al [36] , developed a hierarchical adaptive deep convolutional neural network for bearing fault diagnosis. ...
doi:10.1007/s10033-017-0150-0
fatcat:7e7qq3xewzhqtdvb2np6q3ftv4
2021 Index IEEE Transactions on Industrial Informatics Vol. 17
2021
IEEE Transactions on Industrial Informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TII Jan. 2021 720-727 An Explainable Convolutional Neural Network for Fault Diagnosis in Linear Motion Guide. ...
., +, TII June 2021 4117-4126 An Explainable Convolutional Neural Network for Fault Diagnosis in Linear Motion Guide. ...
doi:10.1109/tii.2021.3138206
fatcat:ulsazxgmpfdmlivigjqgyl7zre
Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review
2021
Journal of Vibroengineering
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There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. ...
Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. ...
[64] solve the problem of information losses when using the fusion process through an ensemble CNN model for diagnosing bearing faults, specifically, the model used one multichannel fusion convolutional ...
doi:10.21595/jve.2021.22100
fatcat:erdumydfzvg7bagqjfggy256my
Special Issue on Computational Intelligence for Healthcare
2021
Electronics
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Convolutional Neural Network" [11] . ...
A onedimensional convolutional neural network (CNN) has been used for the prediction tasks. ...
doi:10.3390/electronics10151841
fatcat:6idy2z7ixrh73fre3feocf55u4
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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Neural Network Huanjie Wang, Xiwei Bai and Jie Tan .......... 2893 Convolutional Neural Network for Blur Images Detection as an Alternative for Laplacian Method Tomasz Szandala .......... 2901 Mild Cognitive ...
Mormille and Masayasu Atsumi .......... 2670 Evolving Optimal Convolutional Neural Networks Subhashis Banerjee and Sushmita Mitra .......... 2677 GPCNN: Evolving Convolutional Neural Networks using Genetic ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems
2022
Sustainability
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This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. ...
The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis. ...
A convolutional neural network (CNN) is used to classify faults in this case. ...
doi:10.3390/su141710518
fatcat:uiluwnr7efepdhe3fs2sivvd6u
Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification
2023
Electronics
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Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. ...
Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. ...
The common intelligent diagnosis methods are neural networks, support vector machines, evolutionary intelligence, expert systems, fuzzy faults, information fusion diagnosis methods, etc. ...
doi:10.3390/electronics12030511
fatcat:zdntifh4pbe37o4vqfpy5dawfi
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