Skip to main content
SpringerLink
Log in

Optimal fuzzy attention deep learning enabled rotating machine fault diagnosis for sustainable manufacturing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Fault diagnoses of rotating machinery (RM) have played an important role in the safety and reliability of contemporary sustainable manufacturing systems. Extracting features from original signal is a fundamental process for conventional fault recognition performance which needs human intervention and expert knowledge. This article introduces a Modified Moth Flame Optimization with Fuzzy Attention Deep Learning Enabled Fault Diagnosis (MMFO-FADLFD) Model for RMs for the identification and classification of faults. It follows empirical mode decomposition (EMD)–based signal decomposition and principal component analysis (PCA)–based feature reduction processes and fuzzy attention based bidirectional long short term memory (FA-BLSTM) model. Further, the MMFO algorithm is applied as a hyperparameter tuning technique for enhanced fault classification outcomes. The experimental validation of the MMFO-FADLFD model is tested using a dataset and the outcomes are examined under varying aspects and it confirms a promising performance of the MMFO-FADLFD model over other recent methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Manikandan S, Duraivelu K (2021) Fault diagnosis of various rotating equipment using machine learning approaches–a review. Proc Inst Mech Eng Part E: J Process Mech Eng 235(2):629–642

    Article  Google Scholar 

  2. Saha DK, Hoque ME, Badihi H (2022) Development of intelligent fault diagnosis technique of rotary machine element bearing: a machine learning approach. Sensors 22(3):1073

    Article  Google Scholar 

  3. Tang S, Yuan S, Zhu Y (2019) Deep learning-based intelligent fault diagnosis methods toward rotating machinery. Leee Access 8:9335–9346

  4. Almounajjed A, Sahoo AK, Kumar MK, Alsebai MD (2021) Investigation techniques for rolling bearing fault diagnosis using machine learning algorithms. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp 1290–1294

  5. Zhao X, Jia M (2020) A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery. Struct Health Monit 19(6):1745–1763

    Article  Google Scholar 

  6. Chen Z, Mauricio A, Li W, Gryllias K (2020) A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mech Syst Signal Process 140:106683

    Article  Google Scholar 

  7. Piltan F, Prosvirin AE, Jeong I, Im K, Kim JM (2019) Rolling-element bearing fault diagnosis using advanced machine learning-based observer. Appl Sci 9(24):5404

    Article  Google Scholar 

  8. Mushtaq S, Islam MM, Sohaib M (2021) Deep learning aided data-driven fault diagnosis of rotatory machine: a comprehensive review. Energies 14(16):5150

    Article  Google Scholar 

  9. Tran MQ, Elsisi M, Mahmoud K, Liu MK, Lehtonen M, Darwish MM (2021) Experimental setup for online fault diagnosis of induction machines via promising IoT and machine learning: towards industry 4.0 empowerment. IEEE access 9:115429–115441

    Article  Google Scholar 

  10. Ince T, Malik J, Devecioglu OC, Kiranyaz S, Avci O, Eren L, Gabbouj M (2021) Early bearing fault diagnosis of rotating machinery by 1d self-organized operational neural networks. IEEE Access 9:139260–139270

    Article  Google Scholar 

  11. Choudhary A, Mian T, Fatima S (2021) Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Measurement 176:109196

    Article  Google Scholar 

  12. Kolar D, Lisjak D, Pająk M, Gudlin M (2021) Intelligent fault diagnosis of rotary machinery by convolutional neural network with automatic hyper-parameters tuning using Bayesian optimization. Sensors 21(7):2411

    Article  Google Scholar 

  13. Niu G, Wang X, Golda M, Mastro S, Zhang B (2021) An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis. Neurocomputing 445:26–34

    Article  Google Scholar 

  14. Li G, Wu J, Deng C, Xu X, Shao X (2021) Deep reinforcement learning-based online domain adaptation method for fault diagnosis of rotating machinery. IEEE/ASME Trans Mechatronics

  15. Lee YE, Kim BK, Bae JH, Kim KC (2021) Misalignment detection of a rotating machine shaft using a support vector machine learning algorithm. Int J Precis Eng Manuf 22(3):409–416

    Article  Google Scholar 

  16. Tayyab SM, Asghar E, Pennacchi P, Chatterton S (2020) Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection. Procedia Manuf 51:266–273

    Article  Google Scholar 

  17. Dai W, Mo Z, Luo C, Jiang J, Zhang H, Miao Q (2020) Fault diagnosis of rotating machinery based on deep reinforcement learning and reciprocal of smoothness index. IEEE Sens J 20(15):8307–8315

    Article  Google Scholar 

  18. Liu J, Zhou K, Yang C, Lu G (2021) Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning. Front Mech Eng 16(4):829–839

    Article  Google Scholar 

  19. Barbosh M, Singh P, Sadhu A (2020) Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Mater Struct 29(9):093001

    Article  Google Scholar 

  20. Ejaz MS, Islam MR, Sifatullah M, Sarker A (2019) Implementation of principal component analysis on masked and non-masked face recognition. In 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT) (pp. 1–5). IEEE

  21. Lu H, Yang R, Deng Z, Zhang Y, Gao G, Lan R (2021) Chinese image captioning via fuzzy attention-based DenseNet-BiLSTM. ACM Trans Multimed Comput Commun Appl (TOMM) 17(1s):1–18

    Article  Google Scholar 

  22. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  23. Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2020) Moth–flame optimization algorithm: variants and applications. Neural Comput Appl 32(14):9859–9884

    Article  Google Scholar 

  24. Li LL, Wen SY, Tseng ML, Wang CS (2019) Renewable energy prediction: a novel short-term prediction model of photovoltaic output power. J Clean Prod 228:359–375

    Article  Google Scholar 

Download references

Funding

We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP- 2020/150), Taif University, Taif, Saudi Arabia.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Fawaz Alassery

  2. Department of Management Information System, College of Business Administration, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Lamia Alhazmi

Authors
  1. Fawaz Alassery
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lamia Alhazmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Author 1: Fawaz Alassery

☒Conceived and design the analysis.

☒ Collecting the Data.

☒ Wrote the Paper.

Author 2: Lamia Alhazmi

☒ Contributed data and analysis tools.

☒ Performed and analysis.

☒ Manuscript Editing and Figure Design.

Corresponding author

Correspondence to Lamia Alhazmi.

Ethics declarations

Ethics approval

This article does not contain any studies with animals performed by any of the authors.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alassery, F., Alhazmi, L. Optimal fuzzy attention deep learning enabled rotating machine fault diagnosis for sustainable manufacturing. Int J Adv Manuf Technol (2022). https://doi.org/10.1007/s00170-022-10512-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00170-022-10512-9

Keywords

Search

Navigation