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Gear Fault Detection in a Planetary Gearbox Using Deep Belief Network

Hu Hao, Feng Fuzhou, Jiang Feng, Zhou Xun, Zhu Junzhen, Xue Jun, Jiang Pengcheng, Li Yazhi, Qian Yongchan, Sun Guanghui, Chen Caishen, Juan P. Amezquita-Sanchez
2022 Mathematical Problems in Engineering  
To address these problems, a fault diagnosis method of planetary gearbox based on deep belief networks (DBNs) is proposed.  ...  Traditional prognostics and health management (PHM) methods for fault detection require complex signal processing and manual fault feature extraction, and the accuracy is low.  ...  [45] proposed a novel bearing fault diagnosis method based on switchable normalization semisupervised generative adversarial networks (SN-SSGANs).  ... 
doi:10.1155/2022/9908074 fatcat:op5uuladtvc7vmtdppozfc6kdu

Guest Editorial: Special Section on Resilience, Reliability, and Security in Cyber–Physical Systems

Bin Zhang, Peng Zhang, Tuyen Vu, Mo-Yuen Chow
2020 IEEE Transactions on Industrial Informatics  
The article "FEM simulation-based generative adversarial networks to detect bearing faults" by Gao et al. proposes a new fault-detection method to address the problem of data insufficiency by running a  ...  Then, generative adversarial networks (GANs) are utilized, along with simulation and measurement fault data, to obtain synthetic samples for artificial intelligence based fault detection.  ... 
doi:10.1109/tii.2020.2971725 fatcat:rky3mouewzbenlk6mz56ss2n6m

A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis

Lin Huo, Huanchao Qi, Simiao Fei, Cong Guan, Ji Li, Shahid Mumtaz
2022 Computational Intelligence and Neuroscience  
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  ...  Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data.  ...  [20] used GAN to generate a large number of fault samples and applied them to fault diagnosis based on FEM simulation and reflected the practicability of GAN from the side through the simulation results  ... 
doi:10.1155/2022/7592258 pmid:35875772 pmcid:PMC9300344 fatcat:7sljsevv6jhazeo7ir772x5jja

Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics

Chenyu Liu, Alexandre Mauricio, Junyu Qi, Dandan Peng, Konstantinos Gryllias
2020 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
A Domain Adversarial Neural Network (DANN) is used to achieve the domain adaptation target between the simulation and the real data.  ...  Based on real bearing run-to-failure experiments, the performance of the proposed method is validated with high RUL prediction accuracy.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the support of the China Scholarship Council, Flanders Make the strategic research centre for the manufacturing industry in the context of DGTwin Prediction  ... 
doi:10.36001/phmconf.2020.v12i1.1294 fatcat:ikpu336defclhhi7rupncifqki

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 personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an FEM simulation driving support vector machine is proposed [39] .  ...  A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine is proposed [38] .  ... 
doi:10.1109/access.2020.3025956 fatcat:o7bno6xeojcx3fyr55xsjiyq2q

Review of Vibration-Based Structural Health Monitoring Using Deep Learning

Gyungmin Toh, Junhong Park
2020 Applied Sciences  
A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis.  ...  The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness.  ...  The performance of the CNN layer was varied, and the deep adversarial convolutional neural network (DACNN), which is the deep learning model of the CNN combined with the generative adversarial network  ... 
doi:10.3390/app10051680 fatcat:4vgiycrznvgcjfsv6fshlc3seq

Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning

Sihan Wang, Dazhi Wang, Deshan Kong, Jiaxing Wang, Wenhui Li, Shuai Zhou
2020 Sensors  
However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization  ...  Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples.  ...  In the field of bearing fault diagnosis, Zhang [24] performed data augmentation by manually copying and intercepting the original signal, Li [25] used Generative Adversarial Networks (GAN) to solve  ... 
doi:10.3390/s20226437 pmid:33187173 fatcat:ilow3gnaavd6jhpkre3nyyxpui

Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research [article]

Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat, Ketan Kotecha, Ajith Abraham
2022 arXiv   pre-print
It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field.  ...  The challenging task in PDM is to diagnose the type of fault.  ...  signals to improve bearing fault detection accuracy.  ... 
arXiv:2206.14153v1 fatcat:qqcznet7cjb63ioed3rh77cv5q

A Novel Data Augmentation Method for Intelligent Fault Diagnosis under Speed Fluctuation Condition

Xiaoyu Wang, Zhenyun Chu, Baokun Han, Jinrui Wang, Guowei Zhang, Xingxing Jiang
2020 IEEE Access  
(SNLLP) health indicator and used to detect incipient fault.  ...  [20] presented super-resolution generative adversarial network (SRGAN), a generative adversarial network (GAN) for image SR; this framework is the first that can infer photorealistic natural images  ... 
doi:10.1109/access.2020.3014340 fatcat:qwcxgtjrbvc5jda5lm6i3avcfa

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Diagnosis in Photovoltaic Arrays Using GBSSL Method and Proposing a Fault Correction System; TII Aug. 2020 5300-5308  ...  . 2020 743-753 Mohammadi-Ivatloo, B., see Daneshvar, M., TII June 2020 3928-3941 Mohtat, P., see Lee, S., TII May 2020 3376-3386 Momeni, H., Sadoogi, N., Farrokhifar, M., and Gharibeh, H.F., Fault  ...  ., +, TII Dec. 2020 7479-7488 FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Maria Drakaki, Yannis L. Karnavas, Ioannis A. Tziafettas, Vasilis Linardos, Panagiotis Tzionas
2022 Journal of Industrial Engineering and Management  
Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs).  ...  Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis  ...  Zhang and Bai (2021)020)j, Venkatraman, Sivakumar, Prasanna & Shankar (2020)ring joints based on deep convolutional generative adversarial network (DCGAN) to obtain a balanced dataset and CNN networks  ... 
doi:10.3926/jiem.3597 fatcat:o54davj3mnhf5ghbbual6bvrsi

A Hybrid Method to Diagnose 3D Rotor Eccentricity Faults in Synchronous Generators Based on ALIF_PE and KFCM

Chao Zhang, Chuang-Jin He, Shuai Li, Yu-Ling He, Xiao-Long Wang, Xiang-Yu Liu, Lun Cheng, Francisco Beltran-Carbajal
2021 Mathematical Problems in Engineering  
The result indicates that the classification coefficient based on ALIF and KFCM behaves closer to 1, while the average fuzzy entropy (FE) is closer to 0, showing that this method is able to detect different  ...  Firstly, adaptive local iterative filtering (ALIF) method was used to decompose the vibration signals of the generator under eccentricity faults.  ...  adversarial networks (GANs) for rotor-bearing systems to expand fault samples so as to improve the accuracy.  ... 
doi:10.1155/2021/5513881 fatcat:6e2zt7fy2vdavpxxa5m7ymk3qe

USYS 2018 List of Papers and Authors

2018 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS)  
Diagnosis Framework for a Gearbox Based on Generative Adversarial Nets Pengfei Liang (School of Mechanical Science and Engineering, Huazhong University of Science and Technology); Chao Deng (Huazhong  ...  Real-time intelligent fault diagnosis using deep convolutional neural networks and wavelet transform Study of Drag Reduction on Grooved Surface Yuze Jiang (Huazhong University of Science and Technology  ... 
doi:10.1109/usys.2018.8778923 fatcat:tyc2tjbchzfkjbi3vkrb6ldn4a

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Monica Tiboni, Carlo Remino, Roberto Bussola, Cinzia Amici
2022 Applied Sciences  
In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components.  ...  The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for  ...  on the finite element method (FEM) used to simulate vibration responses.  ... 
doi:10.3390/app12030972 fatcat:g2pgnwcfbbe7lbjeehvrbp36nq

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic [article]

Barbara Cunha
2022 arXiv   pre-print
Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design.  ...  In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules.  ...  Zhao and Yuan [89] implemented a CNN which detects and classifies faults in the outer race, inner race, and the cage of a bearing and, once the fault is detected, the DT predicts its RUL in real-time  ... 
arXiv:2204.06362v1 fatcat:ayn6cpcn7nd65hum3z4fspxwrm
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