Using Long-Short Term Memory Networks with Genetic Algorithm to Predict Engine Condition

Abstract

Predictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn’t only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural network (DNN) architecture proposed in this study intends to bring a different approach to the predictive maintenance domain. There is an input layer in this architecture, a Long-Short term memory (LSTM) layer, a dropout layer (DO) followed by an LSTM layer, a hidden layer, and an output layer. The number of epochs used in the architecture and the batch size was determined using the Genetic Algorithm (GA). The activation function used after the output layer, DO ratio, and optimization algorithm optimizes loss function determined by using grid search (GS). This approach brings a different perspective to the literature for finding optimum parameters of LSTM. The neural network and hyperparameter optimization approach proposed in this study performs much better than existent studies regarding LSTM network usage for predictive maintenance purposes.

Keywords

• Deep learning
• Genetic algorithm
• Artificial neural networks
• Predictive maintenance
• Cost efficient maintenance

References

1. [1] Ran, Y., Xin Z., Pengfeng L., Yonggang W., and Ruilong D.. “A survey of predictive maintenance: Systems, purposes and approaches”, arXiv, (2019).
2. [2] Zhou, Q., Junbo S., Shiyu Z., Xiaofeng M., and Mutasim S., “Remaining useful life prediction of individual units subject to hard failure”, IIE Transactions 46, (10): 1017-1030, (2014).
3. [3] Jardine, A. KS, Daming L., and Dragan B., “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, 20(7): 1483-1510, (2006).
4. [4] Heimes, F. O., “Recurrent neural networks for remaining useful life estimation”, International conference on prognostics and health management, United States, 1-6, (2008).
5. [5] Porotsky, S., and Zigmund B., “Remaining useful life estimation for systems with non-trendability behaviour”, IEEE Conference on Prognostics and Health Management, United States, 1-6, (2012).
6. [6] Jain, A. K., Pradeep K., and Bhupesh K. L., “Prediction of Remaining Useful Life of an Aircraft Engine under Unknown Initial Wear”, 5th international & 26th all India manufacturing technology, design and research conference, Guwahati, India, (1–5), (2014).
7. [7] Babu, G.S., Li, X.L. and Suresh, S, “Meta-cognitive regression neural network for function approximation: application to remaining useful life estimation”, International Joint Conference on Neural Networks (IJCNN), Canada, 4803-4810, (2016).
8. [8] dos Santos Lima, F.D., Amaral, G.M.R., de Moura Leite, L.G., Gomes, J.P.P. and de Castro Machado, J. “Predicting failures in hard drives with lstm networks”, Brazilian Conference on Intelligent Systems (BRACIS), Brazil, 222-227, (2017).

9. [9] Aydin, O. and Guldamlasioglu, S., “Using LSTM networks to predict engine condition on large scale data processing framework”, 4th International Conference on Electrical and Electronic Engineering (ICEEE), Turkey, 281-285, (2017).
10. [10] Zaifa C., Yan-cheng L. and Siyuan L. “Mechanical State Prediction Based on LSTM Neural Network”, 36th Chinese Control Conference (CCC 2017), China, 3871-3876, (2017).
11. [11] Das A., Mueller F., Siegel C. and Vishnu A., “Desh: deep learning for system health prediction of lead times to failure in HPC”, 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC ’18), United States, 40 - 51, (2018).
12. [12] Cachada, A., Barbosa, J., Leitño, P., Gcraldcs, C. A., Deusdado, L., Costa, J. and Romero, L., “Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture”, 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Portugal, 139-146, (2018).
13. [13] Öztanır O., “Makine Öğrenmesi Kullanılarak Kestirimci Bakım”, (Msc.), Hacettepe University, Turkey, (2018).
14. [14] Zhang, W., Wuwu G., Xin L., Yan L., Jiehan Z., Bo L., Qinghua L., and Su Y,. “LSTM-based analysis of industrial IoT equipment.”, IEEE Access, (6): 23551-23560, (2018).
15. [15] Baker H., “The Impact of Digital on Unplanned Downtime: An Offshore Oil And Gas Perspective”, https://www.ge.com/digital/sites/default/files/download_assets/ge-the-impact-of-digital-on-unplanned-downtime.pdf, (2016).
16. [16] Milojevic, D. M., and Nassah, F., Digital Industrial Revolution with Predictive Maintenance, General Electrics, https://www.ge.com/digital/sites/default/files/download_assets/PAC_Predictive_Maintenance_GE_Digital_Executive_Summary_2018_1.pdf, (2018).
17. [17] Hochreiter S and Schmidhuber J., “Long Short-Term Memory”, Neural Computation, 9(8): 1735-1780, (1997).
18. [18] Koza, J. R., “Genetic Programming: on the Programming of Computers by Means of Natural Selection”, MIT Press, Cambridge, (1992).
19. [19] Goldberg, D. E. and Holland, J. H., “Genetic algorithms and machine learning, Machine Learning”, 3(2): (95–99), (1998).
20. [20] Goldberg, D. E., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, New York, (1989).
21. [21] Wicaksono, A. S. and Supianto, A. A., “Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction”, International Journal of Advanced Computer Science And Applications, 9(12): 263-267, (2018).
22. [22] Bouktif, S., Fiaz, A., Ouni, A. and Serhani, M.A., “Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches”, Energies, 11(7): 1636, (2018).
23. [23] Chung, H., and Kyung-shik S., “Genetic algorithm-optimized long short-term memory network for stock market prediction”, Sustainability, 10(10): 3765, (2018).
24. [24] Saxena A, Goebel K, Simon D and Eklund N. “Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation”, International Conference on Prognostics and Health Management, United States, 1-10, (2008).