Predictive maintenance (PdM) represents a transformative approach in modern manufacturing, aiming to forecast equipment failures through data analysis rather than relying on scheduled or reactive maintenance. This research presents a comprehensive deep learning-based framework for the predictive maintenance of Computer Numerical Control (CNC) machine tools, which are critical and costly assets in precision manufacturing. The proposed system utilizes a multi-sensor data fusion strategy, acquiring real-time operational data including vibration, acoustic emission, spindle current, and temperature. A hybrid deep learning model is developed, integrating Convolutional Neural Networks (CNNs) for automatic feature extraction from high-dimensional sensor signals and Long Short-Term Memory (LSTM) networks to capture temporal dependencies and degradation trends. The model is trained on historical run-to-failure data to learn the complex mapping between multi-modal sensor inputs and the Remaining Useful Life (RUL) of critical components such as spindle bearings and ball screws. Experimental validation is conducted on a three-axis CNC milling machine under controlled operational loads. The results demonstrate that the proposed CNN-LSTM model achieves superior predictive accuracy compared to traditional machine learning benchmarks like Support Vector Regression and standalone neural networks. The system successfully identifies incipient fault conditions with a high degree of precision, providing early warnings significantly ahead of functional failure. This capability enables optimal maintenance scheduling, minimizes unplanned downtime, reduces maintenance costs, and extends the operational lifespan of CNC machinery. The study confirms the significant potential of deep learning in enhancing the intelligence and reliability of industrial predictive maintenance systems.

Jing Pu. Research on Predictive Maintenance of CNC Machine Tools Based on Deep Learning. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 1: 47-52. https://doi.org/10.25236/AJETS.2026.090106.

[1] Li, A., Smith, B., & Johnson, C. Advancements in Predictive Maintenance: A Comprehensive Review [J]. Journal of Manufacturing Technology, 2021, 15(3): 45–62.

[2] Zhang, Y., Wang, Q., & Chen, L. Impact of Predictive Maintenance on Manufacturing Efficiency [J]. International Journal of Industrial Engineering, 2022, 28(1): 17–34.

[3] Gupta, R., Patel, S., & Singh, M. Enhancing Predictive Maintenance through Real time IoT Sensor Data Integration [J]. Journal of Industrial Internet of Things, 2021, 12(4): 112–128.

[4] Chen, H., Zhang, L., & Li, Q. Deep Learning Applications in Predictive Maintenance: Unveiling Subtle Patterns for Early Equipment Failure Detection [J]. Journal of Machine Learning Research, 2021, 18(3): 102–118.

[5] Wang, Y., & Liu, M. Improving Prediction Accuracy in Predictive Maintenance: A Comprehensive Study on Ensemble Methods [J]. Expert Systems with Applications, 2022, 29(4): 215–230.

[6] Li, Y., Wang, Y., Xu, H., & Hu, S. J. Cloud manufacturing: A new manufacturing paradigm [J]. Enterprise Information Systems, 2017, 11(5): 779–810.

[7] Lee, J., Kao, H. A., & Yang, S. Service innovation and smart analytics for Industry 4.0 and big data environment [J]. Procedia CIRP, 2014, 16: 3–8.

[8] Wang, D., Chen, G., Lu, C., & Wang, H. Big data analytics in predictive maintenance: A review [J]. IEEE Access, 2018, 6: 69002–69016.

[9] Ribeiro, A., Santos, T., Gomes, T., & Azevedo, A. Machine learning in predictive maintenance: A comprehensive survey [J]. Computers in Industry, 2019, 109: 184–206.

[10] Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. Digital twin-driven product design, manufacturing and service with big data [J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9-12): 3563–3576.

[11] Li, W., Miao, Q., Jiao, J., & Wang, Y. A survey on big data architectures and machine learning algorithms in smart city [J]. Future Generation Computer Systems, 2018, 89: 641–648.

[12] Duan, F., Wang, Y., & Wang, W. Machine learning models in manufacturing [J]. Journal of Manufacturing Systems, 2018, 48: 13–25.