Journal of Production Engineering

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Vol. 29 No. 1 (2026)
Review Article

Artificial intelligence for vibration-based fault diagnosis in rotating machinery

FULL TEXT ARTICLE
  • Idehai O. Ohijeagbon,
  • Frieda Fillemon,
  • Surendra K. Saini
Idehai O. Ohijeagbon
School of Engineering and the Built Environment, Mechanical and Metallurgical Engineering Department, University of Namibia, Ongwediva, Namibia
Frieda Fillemon
School of Engineering and the Built Environment, Mechanical and Metallurgical Engineering Department, University of Namibia, Ongwediva, Namibia
Surendra K. Saini
School of Engineering and the Built Environment, Mechanical and Metallurgical Engineering Department, University of Namibia, Ongwediva, Namibia
DOI: https://doi.org/10.24867/JPE-2026-01-001

Published 2026-06-15

abstract views: 13 // FULL TEXT ARTICLE: 0


Keywords

  • Rotating machinery,
  • Fault diagnosis,
  • Condition monitoring,
  • Artificial intelligence,
  • Deep learning,
  • Digital twin
    • ...More
    Less

How to Cite

O. Ohijeagbon, I., Fillemon, F., & K. Saini, S. (2026). Artificial intelligence for vibration-based fault diagnosis in rotating machinery. Journal of Production Engineering, 29(1), 1–11. https://doi.org/10.24867/JPE-2026-01-001

Copyright (c) 2026 Journal of Production Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Rotating machinery underpins numerous industrial systems, where reliable fault diagnosis is essential for ensuring safety, operational efficiency, and cost reduction. Conventional vibration-based diagnostic methods often struggle with non-stationary, noisy, and high-dimensional data. Recent advances in artificial intelligence (AI), particularly deep learning techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have introduced robust and effective solutions for condition monitoring and fault diagnosis. This systematic review synthesizes recent progress in AI-driven vibration analysis, focusing on feature extraction, fault classification, temporal modeling, and multi-sensor data fusion. Key challenges, including model generalization, interpretability, and data scarcity, are critically examined in the context of industrial deployment. Furthermore, emerging research directions such as explainable AI, domain adaptation, edge computing, and digital twin integration are discussed. By consolidating current knowledge and identifying open research challenges, this review provides a comprehensive reference for the development of intelligent, scalable, and practical fault diagnosis systems for rotating machinery.

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References

  1. Krishnakumari, A. (2016). Fault diagnostics of spur gear using decision tree and fuzzy classifier. International Journal of Advanced Design and Manufacturing Technology, 89: 3487–3494.
  2. Deng, W. (2019). Fault diagnosis of rotating machinery based on convolutional neural networks. Journal of Vibration and Control, 25(6): 910–924.
  3. Yang, M. Y. M. (2022). Bearing vibration signal fault diagnosis based on LSTM-cascade CatBoost. Journal of Internet Technology, 23: 1155–1161.
  4. Zhang, S. (2018). Deep learning-based fault diagnosis in rotating machinery: A review. Mechanical Systems and Signal Processing, 98: 162–183.
  5. Wang, J., Gao, R. X., & Yan, R. (2014). Multi-scale enveloping order spectrogram for rotating machine health diagnosis. Mechanical Systems and Signal Processing, 46: 28–44.
  6. Simon, H. A. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.
  7. Li, C. (2019). Vibration signal analysis using deep learning for fault detection in rotating machines. IEEE Access, 7: 123.
  8. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553): 436–444.
  9. Lee, M. H. (2020). AI-based diagnostics in centrifugal compressors: A comparative study of vibration analysis techniques. Journal of Vibration and Control, 26(6): 978–988.
  10. Salimans, T., & Kingma, D. P. (2016). Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in Neural Information Processing Systems, 901–909.
  11. Pouyanfar, S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5): 1–36.
  12. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61: 85–117.
  13. Amudha, S. C. T. (2022). Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities. International Journal of Imaging Systems and Technology, 32(13).
  14. Olah, S. (2018). LSTM Networks: A detailed explanation. Retrieved October 3, 2024, from https://towardsdatascience.com/lstm-networks-a-detailed-explanation-8fae6aefc7f9
  15. Liu, H. (2016). Intelligent fault diagnosis of rotating machinery using unsupervised deep feature learning. Journal of Mechanical Science and Technology, 30(1): 117–129.
  16. Lei, F. (2020). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. Future Generation Computer Systems, 108: 632–641.
  17. Kannapiran, P., & Subbaraj, B. (2014). Fault detection and diagnosis of pneumatic valve using adaptive neurofuzzy inference system approach. Applied Soft Computing, 19: 362–371.
  18. Basic CNN Architecture Explained. Retrieved October 2, 2024, from https://www.upgrad.com/blog/basic-cnn-architecture
  19. LeCun, Y., Bengio, Y., & LeCun, Y. (1995). Convolutional networks for images, speech, and time series. Handbook of Brain Theory and Neural Networks, 3361–3370.
  20. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, M., Gabbouj, J., & Inman, D. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing.
  21. Datta, G. (2024). Convolutional neural networks. Retrieved October 2, 2024, from https://medium.datadriveninvestor.com/convolutional-neural-networks-3b241a5da51e
  22. Salihu, B., & Tafa, Z. (2020). On computational performances of the actual image classification methods in C# and Python. In Proceedings of the 9th Mediterranean Conference on Embedded Computing (MECO). Budva, Montenegro.
  23. Sabour, S., Frosst, N., & Hinton, G. E. D. (2017). Dynamic routing between capsules. In Advances in Neural Information Processing Systems.
  24. Hinton, G. E. (2009). Deep belief networks. Scholarpedia, 4(5): 5947.
  25. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527–1554.
  26. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.
  27. Izuchukwu, F., & Olorunniwo, A. (1991). Scheduling imperfect preventive and overhaul maintenance. International Journal of Quality & Reliability Management, 8.
  28. Xia, M. (2017). Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Transactions on Mechatronics, 23: 101–110.
  29. Cao, L. (2019). Fault diagnosis of wind turbine gearbox based on deep bi-directional long short-term memory under time-varying non-stationary operating conditions. IEEE Access, 7: 155219–155228.
  30. Dziedziech, K., Jablonski, A., & Dworakowski, Z. (2018). A novel method for speed recovery from vibration signal under highly non-stationary conditions. Measurement, 128: 13–22.
  31. Cardona-Morales, O., Avendaño, L., & Castellanos-Dominguez, G. (2014). Nonlinear model for condition monitoring of non-stationary vibration signals in ship driveline application. Mechanical Systems and Signal Processing, 44(1–2): 134–148.
  32. Li, X. (2020). Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery. Mechanical Systems and Signal Processing, 143: 106825.
  33. Kateris, D., Moshou, D., Pantazi, X. E., Gravalos, I., Sawalhi, N., & Loutridis, S. (2014). A machine learning approach for the condition monitoring of rotating machinery. Journal of Mechanical Science and Technology, 28(1): 61–71.
  34. Xiang, C. (2021). Data-driven fault diagnosis for rolling bearing based on DIT-FFT and XGBoost. Complexity.
  35. Li, Y. (2020). Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chinese Journal of Aeronautics, 33: 427–438.
  36. Martin-Diaz, D. (2018). Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors. ISA Transactions, 80: 427–438.
  37. Wan, L. (2021). An efficient rolling bearing fault diagnosis method based on spark and improved random forest algorithm. IEEE Access, 9: 37866–37882.
  38. Marins, M. A. (2018). Improved similarity-based modeling for the classification of rotating-machine failures. Journal of the Franklin Institute, 355: 1913–1930.
  39. Lei, Y. (2016). Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Oxford, UK: Butterworth-Heinemann.
  40. Sun, W. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement, 89: 171–178.
  41. Li, Z., & Chen, W. (2017). Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 66: 1693–1702.
  42. Shao, L. M., Wang, Y., & Y.-M. (2018). Crack fault classification for planetary gearbox based on feature selection technique and K-means clustering method. Chinese Journal of Mechanical Engineering, 31.
  43. Lu, N., Xiao, Z., & Malik, O. P. (2015). Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 52–53: 393–415.
  44. Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72: 303–315.
  45. Ahmed, H. O. A., Wong, M. D., & Nandi, A. K. (2018). Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features. Mechanical Systems and Signal Processing, 459–477.
  46. Randall, R. B. (2011). Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. Hoboken, NJ, USA: Wiley.