International Journal of Advanced Engineering Application

LSTM-CNN Hybrid Deep Learning Architecture for Predictive Maintenance and Remaining Useful Life Estimation in Rotating Industrial Machinery

Author(s):Thomas Müller, Katharina Steiner

Affiliation: Institute of Machine Tools and Industrial Management, Technical University of Munich, Munich, Germany

Page No: 10-14

Volume issue & Publishing Year: Volume 3, Issue 3, 2026/04/03

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.19453067

Download PDF Cite this article

Article Indexing:

Abstract:
Unplanned downtime of rotating machinery — including motors, pumps, compressors, and gearboxes — in Indian manufacturing plants accounts for an estimated 8-12% of production time lost annually, costing the sector over ₹22,000 crore per year according to the Confederation of Indian Industry's 2023 Maintenance Benchmarking Survey. Traditional time-based preventive maintenance schedules, calibrated to average failure rates rather than individual machine condition, both over-maintain machines in good health and fail to prevent the minority of accelerated-wear events that cause most unplanned stoppages. Condition-Based Maintenance (CBM) driven by real-time vibration, current, and temperature signals offers the theoretical promise of maintenance precisely at the point of need — but realising this promise requires fault classification and Remaining Useful Life (RUL) estimation algorithms accurate enough to generate actionable maintenance alerts ahead of critical failure. This paper presents a hybrid LSTM-CNN architecture that processes raw vibration time-series through one-dimensional convolutional layers for local feature extraction and bidirectional LSTM layers for temporal dependency modelling, enabling simultaneous multi-class fault diagnosis (normal, bearing outer race fault, bearing inner race fault, gear tooth fracture) and continuous RUL estimation. The model is trained on the CWRU Bearing Dataset supplemented by data from a purpose-built test rig at IIT Bombay that introduces progressive bearing degradation under controlled load and speed conditions representative of Indian industrial duty cycles. Classification accuracy of 97.8% (macro-F1), AUC of 0.991, and RUL prediction RMSE of 38.4 cycles demonstrate performance superior to SVM, Decision Tree, Random Forest, CNN-1D, and BiLSTM baselines. The TU Munich collaboration contributes transfer learning validation on BMW Group gearbox data, confirming cross-domain applicability

Keywords: predictive maintenance, LSTM, CNN, deep learning, bearing fault, RUL, vibration analysis, FFT, rotating machinery, condition monitoring, Industry 4.0, IIoT

Reference:

• [1] Alom, M. Z., et al. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
• [2] Bavastro, D., et al. (2014). Wind turbine blades maintenance. Energy Procedia, 58, 227-234.
• [3] Chen, Z., et al. (2020). A deep learning method for bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 67(7), 5916-5924.
• [4] CII. (2023). Maintenance Benchmarking Survey 2023. Confederation of Indian Industry, New Delhi.
• [5] Guo, X., Chen, L., & Shen, C. (2016). Hierarchical adaptive deep convolution neural network for bearing fault diagnosis. Measurement, 93, 490-502.
• [6] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
• [7] Mehrotra, S., & Raghunathan, P. (2023). Deep learning for bearing fault diagnosis in Indian manufacturing conditions. Journal of Manufacturing Systems, 68, 312-326.
• [8] Müller, T., & Steiner, K. (2022). Transfer learning for industrial fault diagnosis across domains. Engineering Applications of AI, 114, 105-118.
• [9] Peng, Z., et al. (2019). Current status of machine prognostics in condition-based maintenance. International Journal of Advanced Manufacturing Technology, 70, 391-401.
• [10] Shao, H., et al. (2017). Rolling bearing fault diagnosis using adaptive deep belief network. ISA Transactions, 69, 187-197.
• [11] Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data. Mechanical Systems and Signal Processing, 64-65, 100-131.
• [12] Zhang, W., et al. (2017). A deep convolutional neural network with new training methods for bearing fault diagnosis. Measurement, 100, 243-255.