Abstract
It is crucial for industrial companies that their systems are available and healthy as most as possible. However, it is inevitable that machines will degrade over time, leading to a fault, or even a complete breakdown if the fault is not identified and addressed in time. In this context, predictive maintenance, i.e., maintenance scheduled and implemented accordingly to the machine’s estimated condition and degradation, is considered a promising approach, as it can extend machines’ availability, productivity, overall product quality, and reduce the waste of material and human resources related to maintenance, among other benefits. It is, for this reason, a key object of Circular Manufacturing, which is an emerging discipline that aims at creating more clean and sustainable manufacturing environments. Deep Learning in this area has been increasingly researched, showing promising results and the ability to extract hidden and abstract information that can improve the performance of health status prediction. In this work, a predictive maintenance approach using Deep Learning is developed for the PRONOSTIA-FEMTO benchmark, regarding the prediction of the current health status of rolling bearing components. The dataset contains vibration data from several run-to-failure experiments. The preprocessing stage is carried out using local mean decomposition, enabling better feature extraction. The approach is then compared to another non-Deep Learning approach for performance assessment.
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References
An, D., Choi, J.H., Kim, N.H.: Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies. Struct Health Monit. Int. J. 17(5), 1095–1109 (2018)
Behzad, M., Arghand, H.A., Bastami, A.R.: Remaining useful life prediction of ball-bearings based on high-frequency vibration features. Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. 232(18), 3224–3234 (2018)
Cao, Q., et al.: KSPMI: a knowledge-based system for predictive maintenance in industry 4.0. Robot. Comput.-Integr. Manuf. 74, 102281 (2022)
Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)
Coble, J., Wesley Hines, J.: Identifying optimal prognostic parameters from data: a genetic algorithms approach. In: Annual Conference of the Prognostics and Health Management Society, PHM 2009, pp. 1–11 (2009)
Li, Y., Si, S., Liu, Z., Liang, X.: Review of local mean decomposition and its application in fault diagnosis of rotating machinery. J. Syst. Eng. Electron. 30(4), 799–814 (2019)
Liu, J., Li, Q., Chen, W., Yan, Y., Qiu, Y., Cao, T.: Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. Int. J. Hydrogen Energy 44, 5470–5480 (2019)
Mao, W., He, J., Tang, J., Li, Y.: Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Adv. Mech. Eng. 10(12), 1–18 (2018)
Mazenko, E.: Preventive vs. Predictive Maintenance: Pros and Cons (2016). https://www.betterbuys.com/cmms/preventive-vs-predictive-maintenance/
Mobley, R.K.: An Introduction to Predictive Maintenance, 2nd edn. Butterworth Heinemann, Oxford (2002)
Nectoux, P., et al.: PRONOSTIA: an experimental platform for bearings accelerated degradation tests. In: IEEE International Conference on Prognostics and Health Management, PHM 2012, pp. 1–8 (2012). http://hal-obspm.ccsd.cnrs.fr/UNIV-BM/hal-00719503
Saidi, L., Ben Ali, J., Bechhoefer, E., Benbouzid, M.: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Appl. Acoust. 120, 1–8 (2017)
Schwendemann, S., Amjad, Z., Sikora, A.: A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Comput. Ind. 125, 103380 (2021)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural. Inf. Process. Syst. 4, 2951–2959 (2012)
Sun, H., Xia, M., Hu, Y., Lu, S., Liu, Y., Wang, Q.: A new sorting feature-based temporal convolutional network for remaining useful life prediction of rotating machinery. Comput. Electr. Eng. 95(September), 107413 (2021)
Thomas, D.S.: The Costs and Benefits of Advanced Maintenance in Manufacturing. National Institute of Standards and Technology (2018)
Wen, Y., Fashiar Rahman, M., Xu, H., Tseng, T.L.B.: Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Meas. J. Int. Meas. Confed. 187, 110276 (2022)
Wu, J., Hua, K., Cheng, Y., Zhu, H., Shao, X., Wang, Y.H.: Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans. 97, 241–250 (2020)
Xu, L., Pennacchi, P., Chatterton, S.: Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. Mech. Syst. Sig. Process. 139, 106617 (2020)
Zhao, S., Zhang, Y., Wang, S., Zhou, B., Cheng, C.: A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method. Measurement 146, 279–288 (2019)
Zheng, Z., Jiang, W., Wang, Z., Zhu, Y., Yang, K.: Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mech. Mach. Theory 91, 151–167 (2015)
Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the Industry 4.0: a systematic literature review. Comput. Ind. Eng. 150, 106889 (2020)
Acknowledgment
This work has been carried out in the H2020 KYKLOS 4.0 project (Grant Agreement Number 872570), which is funded by the European Commission. This work was also partially financed by national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the projects CISUC (UID/CEC/00326/2020) and CTS (UID/EEA/00066/2019).
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Neto, D., Henriques, J., Gil, P., Teixeira, C., Cardoso, A. (2022). A Deep Learning Approach for Data-Driven Predictive Maintenance of Rolling Bearings. In: Brito Palma, L., Neves-Silva, R., Gomes, L. (eds) CONTROLO 2022. CONTROLO 2022. Lecture Notes in Electrical Engineering, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-10047-5_52
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