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A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
2020
Shock and Vibration
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Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. ...
However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. ...
As a result, two hidden layers of MLP are not suitable for bearing fault identification. ...
doi:10.1155/2020/8843759
fatcat:h4zyvhct6nb7lpsj7j5f3yror4
PHM-Qingdao 2019 Author Index
2019
2019 Prognostics and System Health Management Conference (PHM-Qingdao)
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Fault Recognition Method o f Rolling Bearing Fault Diagnosis for Gearbox Based on Deep Belief Network [201923 8] A Robust Fault Diagnosis Method for Rolling Bearings based on Deep Convolutional Neural ...
based on Deep Convolutional Neural Network [2 019180] Fault Diagnosis for Gearbox Based on Deep Belief Network [2 019 2 3 8 ] Wang, Yanxue Stacked Convolutional LSTM Models for Prognosis o f Bearing Performance ...
doi:10.1109/phm-qingdao46334.2019.8942830
fatcat:foexkfircjhvbocvagfdhddsjm
Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm
2022
Computational Intelligence and Neuroscience
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In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ...
ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. ...
a new method of rolling bearing fault diagnosis based on a convolutional neural network. ...
doi:10.1155/2022/8355417
pmid:35880055
pmcid:PMC9308525
fatcat:ialh6ijgcfeqzhjs2oexy6saei
Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks
2021
IET electric power applications
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Recently, convolutional neural networks (CNNs) have redefined the state-of-the-art accuracy for bearing fault detection and identification, extracting location invariant feature mappings without the need ...
A health diagnosis mechanism of rolling element bearings is necessary since the most frequent faults in rotating electrical machines occur in the bearing parts. ...
Artificial neural networks (ANNs) [6] , support vector machines (SVMs) [7] , Bayesian networks (BNs) [8] , neuro-fuzzy inference logic (NFIL) [9] [10] [11] [12] , hidden Markov models (HMMs) [13] ...
doi:10.1049/elp2.12063
fatcat:tvytqfaxlbfpzinnlfyik3axpm
Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review
2022
Journal of Vibroengineering
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Therefore, even a little fault in the rolling element bearings must be recognized and remedied as soon as possible. ...
A rolling element bearing is a common component in household and industrial machines. Even a minor fault in this section has a negative impact on the machinery's overall operation. ...
Using a Two-Dimensional Convolutional Neural Network (2DCNN), X. Peng et al. [101] devised a method for diagnosing rolling bearing faults. For bearing fault diagnostics, A. Khorram et al. ...
doi:10.21595/jve.2022.22366
fatcat:65wxjwjjwzhnjbmynwh2q63zlq
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. ...
Zhou, Convolutional neural network-based hidden Markov models for rolling
element bearing fault identification, Knowl-Based Syst., 144 (2018) 65-76.
[46] W. Gong, H. Chen, Z. Zhang, M. ...
arXiv:2002.07605v1
fatcat:54w3panr35bb7app4y7dfnjeqa
Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network
2020
Shock and Vibration
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In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. ...
Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. ...
Various data-driven prediction methods for the remaining life of mechanical equipment, including the hidden Markov model adopted by Liu et al. [15] , support vector machine (SVM) used by Shen et al. ...
doi:10.1155/2020/8857307
fatcat:qoue72x5mzdehninrpjsjoegsq
Degradation assessment of bearing based on machine learning classification matrix
2021
Eksploatacja i Niezawodnosc
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A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. ...
Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. ...
Artificial neural network was discussed for fault diagnosis of rolling element bearing [21] . ...
doi:10.17531/ein.2021.2.20
fatcat:wcrzvh33m5aqroytctuw76xsne
Multi-source fault identification based on combined deep learning
2020
MATEC Web of Conferences
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This study establishes a multi-source fault identification method based on a combined deep learning strategy to identify a multi-source fault effectively in the fault diagnosis of complex industrial systems ...
In the second state, a model for an ensemble multiple support vector machine classifier is created to recognize fault information. ...
Rolling bearing simulation table. The experiments simulated four bearing operation states: normal (NOR), rolling element fault (Ball), inner ring fault (IR), and outer ring fault (OR). ...
doi:10.1051/matecconf/202030903037
fatcat:32c7j5okf5hw5pm2gturyyezyy
Application of Deep Learning in Fault Diagnosis of Rotating Machinery
2021
Processes
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To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). ...
After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks ...
The authors would also like to thank the reviewers for their valuable suggestions and comments. ...
doi:10.3390/pr9060919
fatcat:bpwam7boe5cpdl4ewffbwjwwzu
Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network
2020
Sensors
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To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). ...
Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning ...
[26] proposed a modified fault diagnosis method combining CNN and hidden markov models (HMM) to classify rolling element bearing faults. Janssens et al. ...
doi:10.3390/s20174930
pmid:32878207
pmcid:PMC7506766
fatcat:7qesp6ztynfqdi7qxjshkue64y
A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
2019
Entropy
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After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based ...
The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. ...
Acknowledgments: Authors are grateful to all the reviewers and the editor for their valuable comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/e21040409
pmid:33267123
pmcid:PMC7514898
fatcat:drfigyw7gzemfirsswsjuzfrii
A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis
2022
Computational Intelligence and Neuroscience
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Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. ...
In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedding, and applied ...
[13] proposed an adaptive deep convolutional neural network for rolling bearing fault diagnosis, which reduces the dependence on manual experience to a certain extent by automatically learning the essential ...
doi:10.1155/2022/7592258
pmid:35875772
pmcid:PMC9300344
fatcat:7sljsevv6jhazeo7ir772x5jja
Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
2020
Applied Sciences
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In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis ...
The performance of the proposed method is evaluated by a real rolling-bearing dataset. ...
For the model selection and construction, machine learning models, such as hidden Markov models [13] , Bayesian networks [14] , neural networks, and support vector machines [15] , are commonly employed ...
doi:10.3390/app10030770
fatcat:sypjkz6tnvg3fhqjmdhfaxy3oq
Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings
2022
Wireless Communications and Mobile Computing
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In this paper, k -nearest neighbor, support vector machines, and convolutional neural networks are successfully applied to the fault diagnosis of bearings, for the benefit of achieving the detection and ...
When convolutional neural networks are used as the early warning model, the accuracy can reach 99.75%. ...
Convolutional Neural Network. ...
doi:10.1155/2022/7611670
fatcat:swk534rphnbdvhn5miwduiynte
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