A COMPARATIVE STUDY ON DATA PRE-PROCESSING TECHNIQUES FOR REMAINING USEFUL LIFE PREDICTION OF TURBOFAN ENGINES

Year 2023, Volume: 6 Issue: 2, 50 - 58, 31.12.2023

Meryem Erdoğan , Muharrem Mercimek

Abstract

This study investigates the use of Long Short-Term Memory (LSTM) for Remaining Usable Life
(RUL) prediction from the Jet Engine Simulated Dataset (C-MAPSS) engine dataset and the impact
and contribution of different data pre-processing techniques on this prediction. After various data
normalization techniques, the dataset is filtered using Savitzky-Golay filtering, wavelet transform and
exponential moving average (EMA). Each filtering technique, together with the normalization
methods, is applied to the data set separately and the effectiveness of the LSTM model in predicting
RUL is evaluated for each combination. Quantitative analysis of the experimental results shows that
appropriate normalization and filtering strategies applied to time series data improve the training
phase of the LSTM models, thereby increasing the accuracy of RUL prediction. In this study, it is
shown that the choice of the best data pre-processing structure will directly affect the efficiency of
network training and thus it is possible to optimize RUL prediction with the LSTM model.

Keywords

C-MAPSS, LSTM, EMA, Savitzky-Golay Filtering, Wavelet transform

References

• Khaled, A., A Hybrid Deep Learning Based Approach for Remaining Useful Life Estimation, IEEE International Conference on Prognostics and Health Management (ICPHM), 2019
• Manuel, A.C., Fusing physics-based and deep learning models for prognostics, Reliability Engineering & System Safety, 2022, 217, 107961
• Zhu, K., Zhang, C., Data-driven RUL Prediction of High-speed Railway Traction System Based on Similarity of Degradation Feature, 9 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), July 05-07, 2019
• Dong, D., Li, X.Y., Life Prediction of Jet Engines Based on LSTM-Recurrent Neural Networks, Prognostics and System Health Management Conference (PHM-Harbin), 2017
• Zhang, Y.Z., Xiong, R., A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction, Prognostics and System Health Management Conference (PHM-Harbin), 2017
• Cui, J., Wang, Y., Prediction of Aeroengine Remaining Useful Life Based on SE-BiLSTM, 34th Chinese Control and Decision Conference (CCDC), 2022
• Xiaoxiong, W., Mingyang, P., and Chunxiao, X., Water Level Data Preprocessing Method Based on Savitzky-Golay Filter, International Conference on Modeling, Simulation and Big Data Analysis (MSBDA), 2019
• Lulu, W., Xiaoming, W., Hongbin, W., Data-driven SOH Estimation of Lithium-ion Batteries Based on Savitzky-Golay Filtering and SSA-SVR Model, IEEE 4th International Conference on Smart Power & Internet Energy Systems, 2022
• Lingyun, S., Ningyun, L., Xianfeng, M., Equipment Health State Assessment Based on MIC-XGBoost, The 13th Asian Control Conference (ASCC), 2022
• Honglin, L., and Wang, J., Based on Wavelet Threshold Denoising-LDA and Bilstm Aircraft Engine Life Prediction, Journal of Phys. Conf. Ser., 2022
• Rai, A., Upadhyay, S.H., The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings, Measurement 111, 2017, 397–410
• Harender, Dr. R. K. Sharma, EEG Signal Denoising based on Wavelet Transform, International Conference on Electronics, Communication and Aerospace Technology ICECA
• Daubechies, I., Ten Lectures on Wavelets, SIAM 1992
• Kopuru, M.S.K., Rahimi, S., Recent Approaches in Prognostics: State of the Art, CSCE, 2019
• Nie, L., Xu, S., Remaining Useful Life Prediction of Aeroengines Based on Multi-Head Attention Mechanism, Machines, 2022, 10(7):552
• Yan, H., Zuo, H., Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion, Hindawi International Journal of Aerospace Engineering, 2021:1-16
• Ye, Z., Yu, J., Health condition monitoring of machines based on long short-term memory convolutional autoencoder, Applied Soft Computing, 2021, 107:107379
• Costa, N., Sánchez, L., Variational encoding approach for interpretable assessment of remaining useful life estimation, Reliability Engineering and System Safety 222, 2022:108353
• Han, J., & Kamber, M., Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011
• Li, J., Jia, Y., Remaining Useful Life Prediction of Turbofan Engines Using CNN-LSTM-SAM Approach, IEEE Sensors Journal, 2023, 23(9)
• Olariu, E.M., Portase, R., Predictive Maintenance- Exploring strategies for Remaining Useful Life (RUL) prediction, IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP), 2022
• De Pater, I., Mitici, M., Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder, Engineering Applications of Artificial Intelligence, 2023, 117:105582
• Ruan, D., Wu, Y., Remaining Useful Life Prediction for Aero-Engine Based on LSTM and CNN, 33rd Chinese Control and Decision Conference (CCDC), 2021
• Zhu, Y., Liu, Z., Aircraft engine remaining life prediction method with deep learning, International Conference on Artificial Intelligence and Computer Information Technology (AICIT), 2022
• Kumari, S., Kumar N., and Rana, P.S., Comparative Performance Study of Different Filtering Techniques with LSTM for the Prediction of Power Consumption in Smart Grid, IETE Journal of Research, 2023
• Jongwoo, B., Filtering Correction Method and Performance Comparison for Time Series Data, Journal of information and communication convergence engineering 2022, 20(2):125-130
• Singh, N., Singh, P., Exploring the effect of normalization on medical data classification, International Conference on Artificial Intelligence and Machine Vision (AIMV), 2021
• Lima, F.T., A Large Comparison of Normalization Methods on Time Series, Big Data Research 2023, 34:100407
• Saxena, A., Goebel, K., Simon D., Eklund, N., Damage propagation modeling for aircraft engine run-to-failure simulation, International Journal of Prognostics and Health Management, 2008, 1(1):9
• Savitzky, A.G., M.J.E., Smoothing and differentiation of data by simplified least squares procedures, Analytical Chemistry, 1964, 36(8):1627-1639
• Hu, W., and Zhao, S., Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network, Front. Energy Res., 2022, 10:969168
• de Miranda, A. R., de Andrade Barbosa, T.M., Conceiçao, A.G.S., Alcalá, S.G.S., Recurrent Neural Network Based on Statistical Recurrent Unit for Remaining Useful Life Estimation, 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019
• Chevalier, G., LARNN: Linear Attention Recurrent Neural Network, 2018
• Hochreiter, S., Schmidhuber, J., Long Short-Term Memory, Neural Computation, 1997, 9(8)
• Berghout, T., Benbouzid, M., A systematic guide for predicting remaining useful life with machine learning, Electronics, 2022, 11(7):1125