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A comprehensive review on convolutional neural network in machine fault diagnosis
[article]
2020
arXiv
pre-print
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To fill in this gap, this work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively. ...
Then, the fundamental theory from the basic convolutional neural network to its variants is elaborated. ...
Han et al. [80] proposed a dynamic ensemble convolutional neural network for gear
fault diagnosis. ...
arXiv:2002.07605v1
fatcat:54w3panr35bb7app4y7dfnjeqa
Application of Rotating Machinery Fault Diagnosis Based on Deep Learning
2021
Shock and Vibration
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After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural ...
networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault ...
Acknowledgments is research work was supported by the Fundamental Research Funds for the Central Universities (Grant no. 00/ 800015A353) and Langfang Science and Technology Support ...
doi:10.1155/2021/3083190
fatcat:4no2xr3f75hszivh7uhq3r2t6y
Convolutional Neural Network in Intelligent Fault Diagnosis toward Rotatory Machinery
2020
IEEE Access
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INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery. 86510 This work is licensed under a Creative Commons Attribution 4.0 License. ...
As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. ...
A new CNN network was established for bearing fault diagnosis, with particle swarm for parameters optimization [42] . ...
doi:10.1109/access.2020.2992692
fatcat:6vfefiu57baujevyizibkn2hju
Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines under Imbalanced Data
2020
IEEE Access
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To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced ...
In the proposed method, a convolutional neural network with the input of multi-sensor signals is used as the base classifier. ...
[10] developed a method using an adaptive learning rate deep belief network combined with Nesterov momentum for rotating machinery fault diagnosis. Fuan et al. ...
doi:10.1109/access.2020.3006895
fatcat:zrhpyilntfcgjnbl6daqsydgiy
Table of Contents
2021
IEEE Transactions on Instrumentation and Measurement
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Coupling Coefficient Estimation of a Nano-g Accelerometer by Continuous Rotation Modulation on a Tilted Rate Table . . ...
Men Rotating Machinery Fault Diagnosis Througha Transformer Convolution Network Subjected to Transfer Learning ................. ...
Leeb A Novel Method for Imbalanced Fault Diagnosis of Rotating Machinery Based on Generative Adversarial Networks .............. ........................................................................ ...
doi:10.1109/tim.2021.3139417
fatcat:qlzrvqfkjffoflc3kte4w6gxy4
Deep ensemble-based classifier for transfer learning in rotating machinery fault diagnosis
2022
IEEE Access
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The ultimate goal is to find an ensemble that can generalize fault diagnosis on rotating machinery for easy implementation and update in industrial settings. ...
The results are compared with classical fault diagnosis classifiers. ...
BACKGROUND A. 1D CONVOLUTIONAL NEURAL NETWORK 1D Convolutional Neural Networks (1D-CNNs) are derived from the ordinary Neural Network with some structural changes. ...
doi:10.1109/access.2022.3158023
fatcat:zjfmqfmyrfaktapyojdsuqszpy
Cross-domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review
2019
IEEE Access
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INDEX TERMS Cross-domain, domain adaptation, fault diagnosis, review, transfer learning. ...
Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. ...
Other methods, such as Deep Inception Net with Atrous Convolution [99] , convolutional neural network based on capsule network [116] , Snapshot Ensemble Convolutional Neural Network [118] , and Noise ...
doi:10.1109/access.2019.2939876
fatcat:ternrenkdjaofl55kwekroaxrm
A Novel Framework for Early Pitting Fault Diagnosis of Rotating Machinery based on Dilated CNN Combined with Spatial Dropout
2021
IEEE Access
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INDEX TERMS Deep learning, dilated convolutional neural network, fault diagnosis, rotating machinery, spatial dropout. ...
Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. ...
[22] proposed an ensemble transfer CNNs driven by multi-channel signals to carry out fault diagnosis of rotating machinery under cross-working conditions. Zhao et al. ...
doi:10.1109/access.2021.3058993
fatcat:yb7qm3f44jfc3pwxriz565g2lq
Rotating Machinery Fault Identification via Adaptive Convolutional Neural Network
2022
Journal of Sensors
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Therefore, this paper proposes an adaptive convolutional neural network (ACNN) by combining ensemble learning and simple convolutional neural network (CNN). ...
Rotating machinery plays an important role in transportation, petrochemical industry, industrial production, national defence equipment, and other fields. ...
Conclusions This paper proposes an adaptive convolutional neural network by combining ensemble learning and simple convolutional neural network. ...
doi:10.1155/2022/6733676
fatcat:p54k6xk62fbzdocya6c4ebo7ui
Deep Convolutional Neural Network using Transfer Learning for Fault Diagnosis
2021
IEEE Access
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A new DL method that combines deep convolutional neural network (DCNN) and transfers learning (TL) for fault diagnosis is proposed in this paper to handle different fault types. ...
INDEX TERMS Fault diagnosis, deep convolutional neural network, transfer learning, image classification, signal processing. ...
DEEP CONVOLUTIONAL NEURAL NETWORK CNN has fewer parameters than fully connected neural networks due to local connections and weight sharing. ...
doi:10.1109/access.2021.3061530
fatcat:gkgg2kekhngongdmqlj5hecbp4
A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition
2022
Shock and Vibration
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Compared with the other four models' experimental results, the model superiority in wear particle identification and classification is verified. ...
Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper ...
Acknowledgments e authors thank the National Natural Science Foundation of China (no. 51975324) and Hubei Key Laboratory Open Fund (nos. 2018KJX10 and 2018KJX03) for supporting this research. ...
doi:10.1155/2022/3696091
fatcat:z2ypgrvn7rax3kddvek7m2lqcu
A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis
[article]
2022
arXiv
pre-print
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Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. ...
Aiming at the common faults of rolling bearings, we propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural ...
National Science Foundation (NSF) for partial funding under award AI Institute in Dynamic Systems (CBET-2112085). ...
arXiv:2203.15275v2
fatcat:golcwf3txjcg5it7b6urll4zdq
Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions
2022
Entropy
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In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural ...
The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods. ...
In addition, some extended models based on standard deep learning models are proposed for rotating machinery fault diagnosis, such as deep convolutional auto-encoder (DCAE) [7] , CNN with capsule network ...
doi:10.3390/e24010119
pmid:35052145
pmcid:PMC8774608
fatcat:dumg35xe7zc4riknbdbqi4vi7a
Table of contents
2020
IEEE Sensors Journal
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Xie 8336 Rub-Impact Fault Diagnosis of Rotating Machinery Based on 1-D Convolutional Neural Networks .............. ........................................................... X. Wu, Z. Peng, J. ...
In Situ Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System ....... ........................................................................ S. Lu, G. Qian, Q. ...
doi:10.1109/jsen.2020.3005052
fatcat:uxfhmdnhmravngv2zg6wss4dge
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. ...
Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. ...
., (2021) proposed a deep adversarial network with joint distribution adaptation for diagnosing rolling bearing transfer faults.To effectively address the aforementioned fault diagnosis issues, a joint ...
doi:10.17676/hae.2021.39.42
fatcat:keuxnwlvnbd27gvxlt5ofv5d7a
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