Abstract
Capturing degradation trends from the Condition monitored signals is a proven technique for predicting the Remining Useful Life (RUL) of the equipment, which has gained more prominence in Prognostics and Health Management (PHM) in Industry 4.0. However, this process is tiresome and comprehending all the physical parameters of the system to construct a Health Index that characterize the health state is a complex process, especially if multiple sensors are involved. This work proposes a Deep residual ensemble model which constructs Fused Health Index (FHI) by harnessing temporal property of signals. The proposed Residual network integrates Bi-directional Long Short Term Memory (Bi-LSTM) and Deep Neural Network (DNN) which absorbs individual residuals of both the forward and reverse LSTMs that acts as an important feature to improve the overall prediction process. The work validated using CMAPPS dataset using various unique performance metrics to portray the effectiveness of the model.
-
Research ethics: Not Applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Carvalho, TP, Soares, FA, Vita, R, Francisco, RDP, Basto, JP, Alcalá, SG. A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 2019;137:106024. https://doi.org/10.1016/j.cie.2019.106024.Search in Google Scholar
2. Sharanya, S. A cyber physical system framework for industrial predictive maintenance using machine learning. In: Real-time applications of machine learning in cyber-physical systems. IGI Global; 2022:241–69 pp.10.4018/978-1-7998-9308-0.ch015Search in Google Scholar
3. https://www.statista.com/statistics/748080/global-predictive-maintenance-market-size/2024.Search in Google Scholar
4. Paolanti, M, Romeo, L, Felicetti, A, Mancini, A, Frontoni, E, Loncarski, J. Machine learning approach for predictive maintenance in industry 4.0. In: 2018 14th IEEE/ASME international conference on mechatronic and embedded systems and applications (MESA). IEEE; 2018:1–6 pp.10.1109/MESA.2018.8449150Search in Google Scholar
5. Kiesewetter, L, Shakib, KH, Singh, P, Rahman, M, Khandelwal, B, Kumar, S, et al.. A holistic review of the current state of research on aircraft design concepts and consideration for advanced air mobility applications. Prog Aero Sci 2023;142:100949. https://doi.org/10.1016/j.paerosci.2023.100949.Search in Google Scholar
6. Bury, CP, Vesely, L, Stoia, M, Fernandez, E, Kapat, J. Impact of sCO2 waste heat recovery system air cooler integration on aircraft engine thrust performance. In: Turbo expo: power for land, sea, and air. American Society of Mechanical Engineers, vol. 86939:V001T01A028 p; 2023.10.1115/GT2023-103166Search in Google Scholar
7. Schreiber, J. Investigation of experimental and numerical methods, and analysis of stator clocking and instabilities in a high-speed multistage compressor [Doctoral dissertation]. Lyon; 2016.Search in Google Scholar
8. Deng, K, Zhang, X, Cheng, Y, Zheng, Z, Jiang, F, Liu, W, et al.. A remaining useful life prediction method with long-short term feature processing for aircraft engines. Appl Soft Comput 2020;93:106344. https://doi.org/10.1016/j.asoc.2020.106344.Search in Google Scholar
9. Kumar, KD. Remaining useful life prediction of aircraft engines using hybrid model based on artificial intelligence techniques. In: 2021 IEEE international conference on prognostics and health management (ICPHM). IEEE; 2021. 1–10 pp.Search in Google Scholar
10. Bai, R, Noman, K, Yang, Y, Li, Y, Guo, W. Towards trustworthy remaining useful life prediction through multi-source information fusion and a novel LSTM-DAU model. Reliab Eng Syst Saf 2024;245:110047. https://doi.org/10.1016/j.ress.2024.110047.Search in Google Scholar
11. Hu, K, Cheng, Y, Wu, J, Zhu, H, Shao, X. Deep bidirectional recurrent neural networks ensemble for remaining useful life prediction of aircraft engine. IEEE Trans Cybern 2021;53:2531–43. https://doi.org/10.1109/tcyb.2021.3124838.Search in Google Scholar
12. Sharanya, S, Venkataraman, R, Murali, G. Predicting remaining useful life of turbofan engines using degradation signal based echo state network. Int J Turbo Jet Engines 2024;40:181–94. https://doi.org/10.1515/tjj-2022-0007.Search in Google Scholar
13. Berghout, T, Mouss, LH, Kadri, O, Saïdi, L, Benbouzid, M. Aircraft engines remaining useful life prediction with an adaptive denoising online sequential Extreme learning machine. Eng Appl Artif Intell 2020;96:103936. https://doi.org/10.1016/j.engappai.2020.103936.Search in Google Scholar
14. Al-Khazraji, H, Nasser, AR, Hasan, AM, Al Mhdawi, AK, Al-Raweshidy, H, Humaidi, AJ. Aircraft engines remaining useful life prediction based on a hybrid model of autoencoder and deep belief network. IEEE Access 2022;10:82156–63. https://doi.org/10.1109/access.2022.3188681.Search in Google Scholar
15. Zeng, J, Cheng, Y. An ensemble learning-based remaining useful life prediction method for aircraft turbine engine. IFAC-PapersOnLine 2020;53:48–53. https://doi.org/10.1016/j.ifacol.2020.11.009.Search in Google Scholar
16. Cheng, Y, Zeng, J, Wang, Z, Song, D. A Health state-related ensemble deep learning method for aircraft engine remaining useful life prediction. Appl Soft Comput 2023;135:110041. https://doi.org/10.1016/j.asoc.2023.110041.Search in Google Scholar
17. Wang, M, Li, Y, Zhang, Y, Jia, L. Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines. Aerospace Systems 2021;4:29–36. https://doi.org/10.1007/s42401-020-00070-x.Search in Google Scholar
18. Lee, J, Mitici, M. Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics. Reliab Eng Syst Saf 2023;230:108908. https://doi.org/10.1016/j.ress.2022.108908.Search in Google Scholar
19. Wang, H, Li, D, Li, D, Liu, C, Yang, X, Zhu, G. Remaining useful life prediction of aircraft turbofan engine based on random forest feature selection and multi-layer perceptron. Appl Sci 2023;13:7186. https://doi.org/10.3390/app13127186.Search in Google Scholar
20. Viale, L, Daga, AP, Fasana, A, Garibaldi, L. Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan. Mech Syst Signal Process 2023;190:110154. https://doi.org/10.1016/j.ymssp.2023.110154.Search in Google Scholar
21. Li, J, Jia, Y, Niu, M, Zhu, W, Meng, F. Remaining useful life prediction of turbofan engines using cnn-lstm-sam approach. IEEE Sensor J 2023;23:10241–51. https://doi.org/10.1109/jsen.2023.3261874.Search in Google Scholar
22. Chen, J, Li, D, Huang, R, Chen, Z, Li, W. Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression. Reliab Eng Syst Saf 2023;234:109151. https://doi.org/10.1016/j.ress.2023.109151.Search in Google Scholar
23. Wen, L, Su, S, Wang, B, Ge, J, Gao, L, Lin, K. A new multi-sensor fusion with hybrid Convolutional Neural Network with Wiener model for remaining useful life estimation. Eng Appl Artif Intell 2023;126:106934. https://doi.org/10.1016/j.engappai.2023.106934.Search in Google Scholar
24. Xu, Z, Zhang, Y, Miao, J, Miao, Q. Global attention mechanism based deep learning for remaining useful life prediction of aero-engine. Measurement 2023;217:113098. https://doi.org/10.1016/j.measurement.2023.113098.Search in Google Scholar
25. Zhang, J, Li, X, Tian, J, Luo, H, Yin, S. An integrated multi-head dual sparse self-attention network for remaining useful life prediction. Reliab Eng Syst Saf 2023;233:109096. https://doi.org/10.1016/j.ress.2023.109096.Search in Google Scholar
26. Zhong, J, Wang, D, Li, C. A nonparametric health index and its statistical threshold for machine condition monitoring. Measurement 2021;167:108290. https://doi.org/10.1016/j.measurement.2020.108290.Search in Google Scholar
27. Peng, K, Pi, Y, Jiao, R, Dong, J, Zhang, K, Zhang, C. Remaining useful life prediction for aircraft engines based on grey model. 2019 Prognostics and system health management conference (PHM-Qingdao); 2019. https://doi.org/10.1109/PHM-Qingdao46334.2019.8943000. Submitted for publication.Search in Google Scholar
28. Babu, GS, Zhao, P, Li, XL. Deep convolutional neural network based regression approach for estimation of remaining useful life. In: 21th international conference on Database systems for advanced applications (DASFAA). Dallas, USA: Spinger; 2016:214–28 pp.10.1007/978-3-319-32025-0_14Search in Google Scholar
29. Malhotra, P, TV, V, Ramakrishnan, A, Anand, G, Vig, L, Agarwal, P, et al.. Multi-Sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. arXiv: 1608.06154; 2016.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Deep residual ensemble model for predicting remaining useful life of turbo fan engines
- Review of gliding arc plasma assisted ignition and combustion for gas turbine application
- Interaction effects between the rotor wake and hub leakage flow in a multi-stage cantilevered compressor
- Determination of the modern marine single shaft gas turbine rotor blades fatigue strength parameters
- Role of different cavity flame holders on the performance characteristics of supersonic combustor
- Thermal performance of teardrop pin fins and zig zag ribs in a wedge channel
- Investigation of varying tip clearance gap and operating conditions on the fulfilment of low-speed axial flow fan
- Upgraded one-dimensional code for the design of a micro gas turbine mixed flow compressor stage with various crossover diffuser configurations
- Performance evaluation of a scramjet engine utilizing varied cavity aft wall divergence with parallel injection in a reacting flow field
- Speed controller design for turboshaft engine using a high-fidelity AeroThermal model
- Multi-nozzle thrust matching control of STOVL engine
- Numerical analysis of brush seal hysteresis based on orthogonal test method
- Conjugated heat transfer characteristics of ribbed-swirl cooling in turbine blade under rotation condition
- Flow pattern formation due to the interdependency of multi-component interactions and their impact on the performance of turbine and exhaust duct of gas turbine
- Flight trajectory optimization study of a variable-cycle turbine-based combined cycle engine hypersonic vehicle based on airframe/engine integration
- Constant temperature line identification on prototype gas turbine combustor with multi-colour change coating
- Sensitivity analysis of aero-engine performance optimization control system and its hardware test verification
- Compensator based improved model predictive control for Aero-engine
- Ignition experimental study based on rotating gliding arc
Articles in the same Issue
- Frontmatter
- Deep residual ensemble model for predicting remaining useful life of turbo fan engines
- Review of gliding arc plasma assisted ignition and combustion for gas turbine application
- Interaction effects between the rotor wake and hub leakage flow in a multi-stage cantilevered compressor
- Determination of the modern marine single shaft gas turbine rotor blades fatigue strength parameters
- Role of different cavity flame holders on the performance characteristics of supersonic combustor
- Thermal performance of teardrop pin fins and zig zag ribs in a wedge channel
- Investigation of varying tip clearance gap and operating conditions on the fulfilment of low-speed axial flow fan
- Upgraded one-dimensional code for the design of a micro gas turbine mixed flow compressor stage with various crossover diffuser configurations
- Performance evaluation of a scramjet engine utilizing varied cavity aft wall divergence with parallel injection in a reacting flow field
- Speed controller design for turboshaft engine using a high-fidelity AeroThermal model
- Multi-nozzle thrust matching control of STOVL engine
- Numerical analysis of brush seal hysteresis based on orthogonal test method
- Conjugated heat transfer characteristics of ribbed-swirl cooling in turbine blade under rotation condition
- Flow pattern formation due to the interdependency of multi-component interactions and their impact on the performance of turbine and exhaust duct of gas turbine
- Flight trajectory optimization study of a variable-cycle turbine-based combined cycle engine hypersonic vehicle based on airframe/engine integration
- Constant temperature line identification on prototype gas turbine combustor with multi-colour change coating
- Sensitivity analysis of aero-engine performance optimization control system and its hardware test verification
- Compensator based improved model predictive control for Aero-engine
- Ignition experimental study based on rotating gliding arc