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Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Abstract

Fault detection has taken on critical relevance in today’s automated manufacturing processes. Defect tolerance, dependability, and safety are some of the fundamental design attributes of complex engineering systems provided by this method. Fault Diagnosis is made more difficult by a lack of performance; data-driven design and the capacity to transfer learning are also essential considerations. This paper proposes the ResNet-based deep learning multilayer fault detection model (ResNet-DLMFDM) to enrich high performance, design, and transmission-learning skills. Wavelet pyramid packet decomposition and each sub drive coefficient utilize the input of each deep research network channel for multi-kernel domain analysis. Pseudo-label networks have been developed conceptually to investigate different interval lengths of sequential functionality and to gather local database flow sequence functions to improve existing error detection processes. Experiment findings reveal that the proposed approach outperforms current algorithms regarding data correctness, storage space utilization, computational complexity, noiselessness, and transfer performance. The results are obtained by analyzing the multi-kernel and showing the domain ratio of 87.6%, increased storage space ratio of 88.6%, wavelet decomposition performance ratio of 84.5%, and the high accuracy of the data transmission ratio of 83.5%, and the noiseless diagnosis ratio of 93.8%.

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Data availability

Not Applicable.

Abbreviations

ResNet-DLMFDM:

ResNet-based deep learning multilayer fault detection model

DDFD:

Data-driven Data Fault Diagnosis

DL:

Deep learning

ND:

Multiple diagnostic tools

PAFD:

Photovoltaic array fault diagnosis

PV:

Photovoltaic

SPT:

Signal processing technology

TM:

Traditional method

DRN:

Deep residual networks

CNN:

Convolutional neural networks

ANN:

Artificial neural networks

LTE:

Long-term evolution networks

OFDM:

Orthogonal frequency division multiplex

DCNN:

Deep Convolutional Neural Network

LSTM:

Long-short term memory

MLP:

Multilayer perceptron

RNN:

Recurrent neural network

SVM:

Support vector machine

PE:

Processing element

DNN:

Deep neural networks

PCA:

Principal component analysis

ECG:

Electrocardiogram

PTB:

Physikalisch-Technische Bundesanstalt

MKL:

Multi-kernel learning

ANC:

Active noise control

ANR:

Active noise reduction

DFT:

Discrete Fourier transform

References

  1. Basheer S, Gandhi UD, Priyan MK, Parthasarathy P (2019) Network support data analysis for fault identification using machine learning. Int J Softw Innov (IJSI) 7(2):41–49

    Article  Google Scholar 

  2. Baskar S, Dhulipala VR (2016) Comparative analysis on fault tolerant techniques for memory cells in wireless sensor devices. Asian J Res Soc Sci Humanit 6(cs1):519–528. https://doi.org/10.5958/2249-7315.2016.00980.1

    Article  Google Scholar 

  3. Baskar S, Dhulipala VR (2018) M-CRAFT-modified multiplier algorithm to reduce overhead in fault tolerance algorithm in wireless sensor networks. J Comput Theor Nanosci 15(4):1395–1401. https://doi.org/10.1166/jctn.2018.7249

    Article  CAS  Google Scholar 

  4. Chen Y, Song B, Zeng Y, Du X, Guizani M (2021) Fault diagnosis based on deep learning for a current-carrying ring of a catenary system in sustainable railway transportation. Appl Soft Comput 100:106907

    Article  Google Scholar 

  5. Cheng P, Li B, Jiao B (2021) Bearing Fault Detection Method Based on Improved Convolution Network. In: Advances in Simulation and Process Modelling: Proceedings of the Second International Symposium on Simulation and Process Modelling (ISSPM 2020), Springer Singapore, 2, pp. 459–466

  6. Deng Z, Zhang JW (2020) Learning synergies based in-hand manipulation with reward shaping. CAAI Trans Intell Technol 5(3):141–149. https://doi.org/10.1049/trit.2019.0094

    Article  Google Scholar 

  7. Duan J, Shi T, Zhou H, Xuan J, Wang S (2020) A novel ResNet-based model structure and its applications in machine health monitoring. J Vib Control 27:1036–1050. https://doi.org/10.1177/1077546320936506

    Article  Google Scholar 

  8. Edalatpanah S (2020) Data envelopment analysis based on triangular neutrosophic numbers. CAAI Trans Intell Technol 5(2):94–98. https://doi.org/10.1049/trit.2020.0016

    Article  MathSciNet  Google Scholar 

  9. Ezhilmaran D, Adhiyaman M (2016) Edge Detection Method for Latent Fingerprint Images Using Intuitionistic Type-2 Fuzzy Entropy. Cybern Inf Technol 16(3):205–218

    MathSciNet  Google Scholar 

  10. Feng X, Li J, Hua Z (2020) Guided filter-based multi-scale super-resolution reconstruction. CAAI Trans Intell Technol 5(2):128–140. https://doi.org/10.1049/trit.2019.0065

    Article  Google Scholar 

  11. Fu S, Cai F, Wang W (2020) Fault diagnosis of photovoltaic array based on SE-ResNet. J Phys Conf Ser 1682(1):012004 IOP Publishing. https://doi.org/10.1088/1742-6596/1682/1/012004

  12. Gao M, Chen J, Mu H, Qi D (2021) A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects. Forests 12(2):212

    Article  Google Scholar 

  13. Guo FY, Zhang YC, Wang Y, Ren PJ, Wang P (2021) Fault Diagnosis of Reciprocating Compressor Valve Based on Transfer Learning Convolutional Neural Network. Math Probl Eng 2021:1–13

    Google Scholar 

  14. Hu H, Wang K, Wang J (2021, April) Application of Deep Residual Network in Fault Diagnosis of Wellbore. In: 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, pp. 1036–1039

  15. Jagadeesan A, Prathik A, Tripathy BK (2016) Recent Trends in Spatial Data Mining and Its Challenges. In: Handbook of Research on Computational Intelligence Applications in Bioinformatics. IGI Global, pp 37–54

    Chapter  Google Scholar 

  16. Jiang FC, Hsu CH (2017) Fault-tolerant system design on cloud logistics by greener standbys deployment with Petri net model. Neurocomputing 256:90–100

    Article  Google Scholar 

  17. Jin Y, Qin C, Huang Y, Liu C (2021) Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network. Measurement 173:108500

    Article  Google Scholar 

  18. Khalaf OI, Sulaiman N, Abdulsahib GM (2014) Analyzing Video Streaming Quality by Using Various Error Correction Methods on Mobile Ad hoc Networks in NS2. Int J Eng Res Appl 4(10):172–178

    Google Scholar 

  19. Li C, Li S, Zhang A, He Q, Liao Z, Hu J (2021) Meta-learning for few-shot bearing fault diagnosis under complex working conditions. Neurocomputing 439:197–211

    Article  Google Scholar 

  20. Luo J, Huang J, Ma J, Li H (2021) An evaluation method of conditional deep convolutional generative adversarial networks for mechanical fault diagnosis. J Vib Control 28:1379–1389. https://doi.org/10.1177/1077546321993563

    Article  Google Scholar 

  21. Molano JI, Lovelle JM, Montenegro CE, Granados JJ, Crespo RG (2018) Metamodel for integration of the internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Humaniz Comput 9(3):709–723

    Article  Google Scholar 

  22. Qian L, Pan Q, Lv Y, Zhao X (2022) Fault Detection of Bearing by Resnet Classifier with Model Based Data Augmentation. Machines 10:521

    Article  Google Scholar 

  23. Saravanan V, Santhi R, Kumar P, Kalaiselvi T, Vennila S (2013) Effect of forest fire on microbial diversity of the degraded shola forest ecosystem of Nilgiris Eastern Slope Range. Res J Agric For Sci 1(5):5–8

    Google Scholar 

  24. Sathishkumar VE, Park J, Cho Y (2020 Mar) Using data mining techniques for bike-sharing demand prediction in a metropolitan city. Comput Commun 1(153):353–366

    Google Scholar 

  25. Shakeel PM, Baskar S, Dhulipala VRS, Jaber MM (2018) Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inf Sci Syst 6(1):1–7

    Article  Google Scholar 

  26. Shankar A, Sivakumar NR, Sivaram M, Ambikapathy A, Nguyen TK, Dhasarathan V (2020) Increasing fault tolerance ability and network lifetime with clustered pollination in wireless sensor networks. J Ambient Intell Humaniz Comput 12:2285–2298

    Article  Google Scholar 

  27. Wan L, Zhang G, Li H, Li C (2021) A novel bearing fault diagnosis method using Spark-based parallel ACO-K-Means clustering algorithm. IEEE Access 9:28753–28768

    Article  Google Scholar 

  28. Wang K, Wu J, Zheng X, Jolfaei A, Li J, Yu D (2020) Leveraging Energy Function Virtualization with Game Theory for Fault-Tolerant Smart Grid. IEEE Trans Ind Inform 17:678–687

    Article  Google Scholar 

  29. Wang Y, Yang M, Li Y, Xu Z, Wanga J, Fang X (2021) A Multi-Input and Multi-Task Convolutional Neural Network for Fault Diagnosis based on Bearing Vibration Signal. IEEE Sensors J 21:10946–10956

    Article  ADS  Google Scholar 

  30. Wang C, Xie Y, Zhang D (2021) Deep learning for bearing fault diagnosis under different working loads and non-fault location points. J Low Freq Noise Vib Act Control 40(1):588–600

    Article  Google Scholar 

  31. Wei C, Tao F, Lin Y, Liang X, Wang Y, Li H, Fang J (2019, December) Substation Equipment Thermal Fault Diagnosis Model Based on ResNet and Improved Bayesian Optimization. In: 2019 9th International Conference on Power and Energy Systems (ICPES). IEEE. pp. 1–5

  32. Wen L, Li X, Gao L (2019) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput & Applic:1–14

  33. Xiao F, Cao Z, Jolfaei A (2020) A novel conflict measurement in decision making and its application in fault diagnosis. IEEE Trans Fuzzy Syst 29(1):186–197

    Article  Google Scholar 

  34. Xiong S, Shi T (2020, November) Deep residual network for enhanced fault diagnosis of rotating machinery. J Phys Conf Ser 1707(1):012010 IOP Publishing

    Article  Google Scholar 

  35. Yang CT, Liu JC, Hsu CH, Chou WL (2014) On improvement of cloud virtual machine availability with virtualization fault tolerance mechanism. J Supercomput 69(3):1103–1122. https://doi.org/10.1109/cloudcom.2011.26

  36. Yang B, Li Q, Chen L, Shen C (2020) Bearing Fault Diagnosis Based on Multilayer Domain Adaptation. Shock Vib 2020:1–11

    Article  CAS  Google Scholar 

  37. Yang D, Karimi HR, Sun K (2021) Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw 141:133–144

    Article  PubMed  Google Scholar 

  38. Yao P, Yang S, Li P (2021, March) Fault Diagnosis Based on ResNet-LSTM for Industrial Process. In: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, (Vol. 5, pp. 728–732)

  39. Yu J, Zhou X, Lu L, Zhao Z (2021) Multi-scale Dynamic Fusion Global Sparse Network for Gearbox Fault Diagnosis. IEEE Trans Instrum Meas 70:1–11

    Google Scholar 

  40. Zhang K, Tang B, Deng L, Tan Q, Yu H (2021) A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels. Mech Syst Signal Process 161:107963

    Article  Google Scholar 

  41. Zhao M, Zhong S, Fu X, Tang B, Dong S, Pecht M (2020) Deep residual networks with adaptively parametric rectified linear units for fault diagnosis. IEEE Trans Ind Electron 68(3):2587–2597

    Article  Google Scholar 

  42. Zhao M, Tang B, Deng L, Pecht M (2020) Multiple wavelets regularized deep residual networks for fault diagnosis. Measurement 152:107331

    Article  Google Scholar 

  43. Zhou A, Wang S, Hsu CH, Kim MH, Wong KS (2019) Virtual machine placement with (m, n)-fault tolerance in a cloud data center. Clust Comput 22(5):11619–11631

    Article  Google Scholar 

  44. Zhuang Z, Lv H, Xu J, Huang Z, Qin W (2019) A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions. Appl Sci 9(9):1823

    Article  Google Scholar 

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Code availability

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Funding

No funds, grants were received by any of the authors.

Author information

Authors and Affiliations

  1. Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers and Informatics, Baghdad, Iraq

    Mustafa Musa Jaber

  2. Department of Computer Science, Al-turath University College, Baghdad, Iraq

    Mustafa Musa Jaber

  3. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, 10023, Iraq

    Mohammed Hasan Ali

  4. Directorate of research and development, Ministry of higher education and scientific research, Baghdad, Iraq

    Sura Khalil Abd

  5. Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10011, Iraq

    Mustafa Mohammed Jassim

  6. College of technical engineering, The Islamic University, Najaf, Iraq

    Ahmed Alkhayyat

  7. Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

    Mohammed Sh. Majid

  8. Computer Technical Engineering, Mazaya University College, Thi Qar, Iraq

    Ahmed Rashid Alkhuwaylidee

  9. Department of Electrical and Electronic Engineering, College of Engineering, Gulf University, Sanad, 26489, Kingdom of Bahrain

    Shahad Alyousif

  10. Research centre, University of Mashreq, Baghdad, Iraq

    Shahad Alyousif

Authors
  1. Mustafa Musa Jaber
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  2. Mohammed Hasan Ali
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  4. Mustafa Mohammed Jassim
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All Author are contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

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Correspondence to Mustafa Musa Jaber.

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Jaber, M.M., Ali, M.H., Abd, S.K. et al. Resnet-based deep learning multilayer fault detection model-based fault diagnosis. Multimed Tools Appl 83, 19277–19300 (2024). https://doi.org/10.1007/s11042-023-16233-9

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  • DOI: https://doi.org/10.1007/s11042-023-16233-9

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