Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan

Salih Ermiş; Oğuz Taşdemir; Çağatay Başgül

doi:10.30855/gmbd.070526A01

Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan

Abstract

Unplanned machine downtime in manufacturing facilities leads to significant production losses, rising maintenance costs, and occupational safety risks. This study compares two hybrid deep learning architectures—Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) —for monthly fault frequency prediction using an eight-year operational dataset from the CLKKSM facility. One-dimensional convolutional layers extract local temporal features from the raw time series, which are subsequently encoded by recurrent cells to capture long-range dependencies. Both models were trained on historical data and validated on a held-out period, then evaluated against actual fault events in early 2026. CNN-LSTM consistently outperformed CNN-GRU across all error metrics on the out-of-sample test set. The performance gap is attributed to the four-gate memory mechanism of LSTM, which models medium- and long-range temporal dependencies more effectively than the two-gate GRU structure. Building on CNN-LSTM forecasts, a three-phase predictive maintenance action plan was developed for the April–December 2026 period, incorporating machine-level priority classification, risk-stratified schedules, and trackable Key Performance Indicator (KPI) targets. The proposed methodology bridges the gap between model outputs and field implementation, offering a transferable framework for data-driven predictive maintenance in industrial settings.

Keywords

References

[1] A. K. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mechanical systems and signal processing, vol. 20, no. 7, pp. 1483-1510, 2006. doi: 10.3224/güzelbişi.21331.454
[2] J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, "Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications," Mechanical systems and signal processing, vol. 42, no. 1-2, pp. 314-334, 2014. doi: 10.1016/j.ymssp.2013.06.004
[3] W. Zhang, D. Yang, and H. Wang, "Data-driven methods for predictive maintenance of industrial equipment: A survey," IEEE systems journal, vol. 13, no. 3, pp. 2213-2227, 2019. doi: 10.1109/JSYST.2019.2905565
[4] S. Hochreiter, Long short-term memory, Neural Computation MIT-Press, 1997.
[5] K. Cho et al., "Learning Phrase Representations Using RNN Encoder–Decoder for Statistical Machine Translation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 25–29, 2014, Doha, Qatar, pp. 1724–1734. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL), 2014.
[6] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015. doi:10.1038/nature14539
[7] R. Zhao, R. Yan, J. Wang, and K. Mao, "Learning to monitor machine health with convolutional bi-directional LSTM networks," Sensors, vol. 17, no. 2, p. 273, 2017. doi:10.3390/s17020273
[8] Y. Wu, M. Yuan, S. Dong, L. Lin, and Y. Liu, "Remaining useful life estimation of engineered systems using vanilla LSTM neural networks," Neurocomputing, vol. 275, pp. 167-179, 2018. doi: 10.1016/j.neucom.2017.05.063

[9] X. Li, Q. Ding, and J.-Q. Sun, "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering & System Safety, vol. 172, pp. 1-11, 2018. doi: 10.1016/j.ress.2017.11.021
[10] A. Vaswani et al., "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017.
[11] A. Zeng, M. Chen, L. Zhang, and Q. Xu, "Are Transformers Effective for Time Series Forecasting?," in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23), Feb. 7–14, 2023, Washington, DC, USA, vol. 37, no. 9, pp. 11121–11128. Washington, DC, USA: Association for the Advancement of Artificial Intelligence (AAAI), 2023. doi: 10.1609/aaai.v37i9.26317
[12] Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, and A. K. Nandi, "Applications of machine learning to machine fault diagnosis: A review and roadmap," Mechanical systems and signal processing, vol. 138, p. 106587, 2020. doi: 10.1016/j.ymssp.2019.106587
[13] R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, "Deep learning and its applications to machine health monitoring," Mechanical systems and signal processing, vol. 115, pp. 213-237, 2019. doi: 10.1016/j.ymssp.2018.05.050
[14] R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, and J. Wang, "Machine health monitoring using local feature-based gated recurrent unit networks," IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1539-1548, 2017. doi: 10.1109/TIE.2017.2733438
[15] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "CBAM: Convolutional Block Attention Module," in Proceedings of the 15th European Conference on Computer Vision (ECCV 2018), Sept. 8–14, 2018, Munich, Germany, pp. 3–19. Cham, Switzerland: Springer, 2018. doi: 10.1007/978-3-030-01234-2_1
[16] H. Yu, C. Chen, N. Lu, and C. Wang, "Deep auto-encoder and deep forest-assisted failure prognosis for dynamic predictive maintenance scheduling," Sensors, vol. 21, no. 24, p. 8373, 2021. doi: 10.3390/s21248373
[17] Q. Wen et al., "Time series data augmentation for deep learning: A survey," arXiv preprint arXiv:2002.12478, 2020. doi: 10.24963/ijcai.2021/631
[18] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014. doi: 10.48550/arXiv.1412.6980
[19] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
[20] R. K. Mobley, An introduction to predictive maintenance. Elsevier, 2002.

Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Salih Ermiş ^*
0000-0002-1053-9160
Türkiye

Oğuz Taşdemir
0000-0003-1782-0024
Türkiye

Çağatay Başgül
0009-0009-2198-9122
Türkiye

Publication Date

August 31, 2026

Submission Date

May 4, 2026

Acceptance Date

June 16, 2026

Published in Issue

Year 2026 Volume: 12 Number: 2

DOI

https://doi.org/10.30855/gmbd.070526A01

IZ

https://izlik.org/JA37XD27YX

Cite

RIS / Bibtex

APA

Ermiş, S., Taşdemir, O., & Başgül, Ç. (2026). Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. Gazi Journal of Engineering Sciences, 12(2), 241-254. https://doi.org/10.30855/gmbd.070526A01

AMA

1.Ermiş S, Taşdemir O, Başgül Ç. Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. GJES. 2026;12(2):241-254. doi:10.30855/gmbd.070526A01

Chicago

Ermiş, Salih, Oğuz Taşdemir, and Çağatay Başgül. 2026. “Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan”. Gazi Journal of Engineering Sciences 12 (2): 241-54. https://doi.org/10.30855/gmbd.070526A01.

EndNote

Ermiş S, Taşdemir O, Başgül Ç (August 1, 2026) Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. Gazi Journal of Engineering Sciences 12 2 241–254.

IEEE

[1]S. Ermiş, O. Taşdemir, and Ç. Başgül, “Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan”, GJES, vol. 12, no. 2, pp. 241–254, Aug. 2026, doi: 10.30855/gmbd.070526A01.

ISNAD

Ermiş, Salih - Taşdemir, Oğuz - Başgül, Çağatay. “Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan”. Gazi Journal of Engineering Sciences 12/2 (August 1, 2026): 241-254. https://doi.org/10.30855/gmbd.070526A01.

JAMA

1.Ermiş S, Taşdemir O, Başgül Ç. Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. GJES. 2026;12:241–254.

MLA

Ermiş, Salih, et al. “Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan”. Gazi Journal of Engineering Sciences, vol. 12, no. 2, Aug. 2026, pp. 241-54, doi:10.30855/gmbd.070526A01.

Vancouver

1.Salih Ermiş, Oğuz Taşdemir, Çağatay Başgül. Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. GJES. 2026 Aug. 1;12(2):241-54. doi:10.30855/gmbd.070526A01