Enhancing Predictive Maintenance in Industrial Machinery Using Hybrid Deep Learning Models with Sensor Fusion and Anomaly Detection

Indu Sharma

Enhancing Predictive Maintenance in Industrial Machinery Using Hybrid Deep Learning Models with Sensor Fusion and Anomaly Detection

Authors: Indu Sharma

Published: 2025-05-01 23:25:00.366

Abstract

This research investigates the application of hybrid deep learning models for enhancing predictive maintenance strategies in industrial machinery. The approach integrates sensor fusion techniques to leverage data from multiple sensor modalities (vibration, temperature, pressure) and employs anomaly detection algorithms to identify deviations from normal operating conditions. A hybrid model, combining Convolutional Neural Networks (CNNs) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, is proposed. The model's performance is evaluated on a real-world dataset of industrial pump operations, demonstrating significant improvements in prediction accuracy and reduced false alarm rates compared to traditional methods. The results highlight the potential of the proposed approach for proactive maintenance planning, minimizing downtime, and optimizing operational efficiency.

Keywords

Predictive Maintenance, Deep Learning, Sensor Fusion, Anomaly Detection, Industrial Machinery, Machine Learning, Hybrid Models, Time Series Analysis, Feature Engineering, Condition Monitoring

References

Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510.
Lee, J., Bagheri, B., & Kao, H. A. (2014). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 2(1), 13-16.
Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3(Mar), 1137-1155.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 6645-6649.
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2017). A hybrid intelligent fault diagnosis approach for rotating machinery. Measurement, 108, 274-283.
Li, X., Ding, Q., & Sun, J. Q. (2018). Remaining useful life prediction in prognostics using deep learning approaches. IEEE Transactions on Industrial Electronics, 65(5), 4563-4573.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep transfer learning for intelligent fault diagnosis: Methodology, applications, and challenges. Mechanical Systems and Signal Processing, 119, 210-224.
Das, S., Roy, S., & Kar, S. (2020). A survey on deep learning methods for predictive maintenance. IEEE Transactions on Industrial Informatics, 16(10), 6481-6492.
Pham, D. T., Ghanem, M. M., & Koc, E. (2021). Sensor fusion for condition monitoring and fault diagnosis of rotating machinery: A review. Measurement, 174, 109021.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Chollet, F. (2017). Deep learning with Python. Manning Publications.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Vapnik, V. N. (1998). Statistical learning theory*. Wiley

Download PDF

How to Cite

Indu Sharma, (2025-05-01 23:25:00.366). Enhancing Predictive Maintenance in Industrial Machinery Using Hybrid Deep Learning Models with Sensor Fusion and Anomaly Detection. JANOLI International Journal of Applied Engineering and Management, Volume UIh3MC5UrwhGKptS6jkQ, Issue 2.

ISSN: 3048-6939

JANOLI International Journal of
Applied Engineering and Management ( JIJAEM )