A Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 26, 2024
Tyler Ward
Kouroush Jenab Jorge Ortega-Moody Selva Staub

Abstract

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

Abstract 140 | PDF Downloads 91

##plugins.themes.bootstrap3.article.details##

Keywords

Condition-Based Maintenance, Machine Learning

References
Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2019) Hierarchical fault classification for resource constrained systems. Mechanical Systems and Signal Processing, 134, 1062666. https://doi.org/10.1016/j.ymssp.2019.106266
Adryan, F. A. & Sastra, K. W. (2021). Predictive maintenance for aircraft engine using machine learning: Trends and challenges. International Journal of Aviation Science and Engineering, 3(1), pp. 37-44. https://doi.org/10.47355/AVIA.V3I1.45
Afridi, Y. S., Ahmad, K., & Hassan, L. (2021). Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions. International Journal of Energy Research, 46(15), pp. 21619-21642. https://doi.org/10.1002/er.7100
Ahmad, S., Styp-Rekowski, K., Nedelkoski, S., & Kao, O. (2020). Autoencoder-based condition monitoring and anomaly detection method for rotating machines. 2020 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata50022.2020.9378015
Ahmed, M. S., Al Bloushi, M. A., & Ali, A. (2022). Case study: Application of wireless condition based monitoring by applying machine learning models. In ADIPEC. OnePetro.
Aliyu, R., Mokhtar, A. A., & Hussin, H. (2022) Classification of pump failure using a decision tree technique. Proceedings of the International Conference on Renewable Energy and E-mobility (ICREEM 2022), 319-336. https://doi.org/10.1007/978-981-99-5946-4_26
Allah Bukhsh, Z., Saeed, A., Stipanovic, I., & Doree, A. G. (2019). Predictive maintenance using tree-based classification techniques: A case of railway switches. Transportation Research Part C: Emerging Technologies, 101, 35–54. https://doi.org/10.1016/j.trc.2019.02.001
Alsumaidaee, Y. A. M. et al. (2022). Review of medium-voltage switchgear fault detection in a condition-based monitoring system by using deep learning. Energies, 15(18), 6762. https://doi.org/10.3390/en15186762
Ansari, F., Glawar, R., & Nemeth, T. (2019). Prima: A prescriptive maintenance model for cyber-physical production systems. International Journal of Computer Integrated Manufacturing, 32(4–5), 482–503. https://doi.org/10.1080/0951192x.2019.1571236
Aqueveque, P., Radrigan, L., Pastene, F., Morales, A. S., & Guerra, E. (2021). Data-driven condition monitoring of mining mobile machinery in non-stationary operations using wireless accelerometer sensor modules. IEEE Access, 9, 17365-17381. https://doi.org/10.1109/ACCESS.2021.3051583
Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IOT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
Bacelar, M. (2021). Monitoring bias and fairness in machine learning models: A review. ScienceOpen Preprints.
Bae, S. J., Mun, B. M., Chang, W., & Vidakovic, B. (2019). Condition monitoring of a steam turbine generator using wavelet spectrum based control chart. Reliability Engineering & System Safety, 184, 13-20. https://doi.org/10.1016/j.ress.2017.09.025
BahooToroody, A., De Carlo, F., Paltrinieri, N., Tucci, M., & Van Gelder, P. H. A. J. M. (2020) Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation. Reliability Engineering & System Safety, 201, 106966. https://doi.org/10.1016/j.ress.2020.106966
Berghout, T., Benbouzid, M., Muyeen, S. M., Bentrcia, T., & Mouss, L.-H. (2021). Auto-nahl: A neural network approach for condition-based maintenance of complex industrial systems. IEEE Access, 9, 152829–152840. https://doi.org/10.1109/access.2021.3127084
Campos Olivares, D., Carrasco Muñoz, A., Mazzoleni, M., Ferramosca, A., & Luque Sendra, Amalia. (2023). Screening of machine learning techniques on predictive maintenance: A scoping review. Dyna, 99, pp. 159-165. https://doi.org/10.6036/10950
Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. da, Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Celikmih, K., Inan, O., & Uguz, H. (2020). Failure prediction of aircraft equipment using machine learning with a hybrid data preparation method. Scientific Programming, 2020, 1–10. https://doi.org/10.1155/2020/8616039
Chatterjee, J. & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable and Sustainable Energy Reviews, 144, 111051. https://doi.org/10.1016/j.rser.2021.111051
Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Ying. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, pp. 581-594. https://doi.org/10.1016/j.jmsy.2023.10.010
Chen, P., Ma, Z., Xu, C., Jin, Y., & Zhou, C. (2024). Self-supervised transfer learning for remote wear evaluation in machine tool elements with imaging transmission attenuation. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2024.3382878
Cheng, J., Liu, Y., Li, W., & Li, T. (2023). Deep reinforcement learning for cost-optimal condition-based maintenance policy of offshore wind turbine components. Ocean Engineering, 283, 115062. https://doi.org/10.1016/j.oceaneng.2023.115062
Cheng, Y., Wang, C., Wu, J., Zhu, H., & Lee, C. K. M. (2022). Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Applied Soft Computing, 118, 108507. https://doi.org/10.1016/j.asoc.2022.108507
Ciaburro, G. (2022). Machine fault detection methods based on machine learning algorithms: A review. Mathematical Biosciences and Engineering, 19(11), pp. 11453-11490. https://doi.org/10.3934/mbe.2022534
Çınar, Z. M. et al. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Decker, L., Leite, D., Minarini, F., Tisbeni, S. R., & Bonacorsi, D. (2022). Unsupervised learning and online anomaly detection. International Journal of Embedded and Real-Time Communication Systems, 13(1), 1–16. https://doi.org/10.4018/ijertcs.302112
Doğru, A., Bouarfa, S., Arizar, R., & Aydoğan, R. (2020). Using convolutional neural networks to automate aircraft maintenance visual inspection. Aerospace, 7(12), 171. https://doi.org/10.3390/aerospace7120171
Drakaki, M., Karnavas, Y. L., Tziafettas, I. A., Linardos, V., & Tzionas, P. (2022). Machine learning and deep learning based methods towards Industry 4.0 predictive maintenance in induction motors: State of the art survey. Journal of Industrial Engineering & Management, 15(1), pp. 31-57. https://doi.org/10.3926/jiem.3597
Elbouchikhi, E., Zia, M. F., Benbouzid, M., & El Hani, S. (2021). Overview of signal processing and machine learning for smart grid condition monitoring. Electronics, 10(21), 2725. https://doi.org/10.3390/electronics10212725
Ellefsen, A. L., Æsøy, V., Ushakov, S., & Zhang, H. (2019). A comprehensive survey of prognostics and health management based on deep learning for autonomous ships. IEEE Transactions on Reliability, 68(2), pp. 720-740. https://doi.org/10.1109/TR.2019.2907402
Färber, M., & Tampakis, L. (2023). Analyzing the impact of companies on AI research based on publications. Scientometrics, 129(1), 31–63. https://doi.org/10.1007/s11192-023-04867-3
Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence, 52, pp. 14246-14280. https://doi.org/10.1007/s10489-022-03344-3
Fernandes, J., Reis, J., Melão, N., Teixeira, L., & Amorim, M. (2021). The role of Industry 4.0 and BPMN in the arise of condition-based and predictive maintenance: A case study in the automotive industry. Applied Sciences, 11(8), 3438. https://doi.org/10.3390/app11083438
Ferreira, C. & Gonçalves, G. (2022). Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of Manufacturing Systems, 63, pp. 550-562. https://doi.org/10.1016/j.jmsy.2022.05.010
Fong, S. (2022). Unsupervised methods for condition-based maintenance in non-stationary operating conditions. Doctoral dissertation. University of Waterloo, Waterloo, Ontario, Canada. https://uwspace.uwaterloo.ca/handle/10012/18191
Fredriksson, G. (2022). Prognostics for condition-based maintenance of electrical control units using on-board sensors and machine learning. Master’s thesis. Linköping University, Linköping, Sweden. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1650950&dswid=7028
Gupta, P. et al. (2024). A review on machine learning enhanced predictive maintenance for electric vehicle power electronics: A pathway to improved reliability and longevity. 3rd International Conference on Applied Research and Engineering (ICARAE2023). https://doi.org/10.1051/e3sconf/202450503017
Hamaide, V. & Glineur, F. (2021). Unsupervised minimum redundancy maximum relevance feature selection for predictive maintenance: Application to a rotating machine. International Journal of Prognostics and Health Management, 12(2). https://doi.org/10.36001/ijphm.2021.v12i2.2955
Hong, X., Xu, Z., & Zhang, Z. (2019). Abnormal condition monitoring and diagnosis for coal mills based on support vector regression. IEEE Access, 7, 170488–170499. https://doi.org/10.1109/access.2019.2955249
Huang, Y., Chen, C. -H., & Huang, C. -J. (2019). Motor fault detection and feature extraction using RNN-based variational autoencoder. IEEE Access, 7, 139086-139096. https://doi.org/10.1109/ACCESS.2019.2940769
Huerta Herraiz, Á., Pliego Marugán, A., & García Márquez, F. P. (2020). Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renewable Energy, 153, 334–348. https://doi.org/10.1016/j.renene.2020.01.148
Hussain, J. (2019). Deep learning black box problem (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393479
Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456
Jauro, F., Chiroma, H., Gital, A. Y., Almutairi, M., Abdulhamid, S. M., & Abawajy, J. H. (2020). Deep Learning Architectures in Emerging Cloud Computing Architectures: Recent Development, challenges and next research trend. Applied Soft Computing, 96, 106582. https://doi.org/10.1016/j.asoc.2020.106582
Jenab, K., Ward, T., Isaza, C., Ortega-Moody, J., & Anaya, K. (2024). Intelligence based condition monitoring model. In International Congress and Workshop on Industrial AI (pp. 639-650). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-39619-9_47
Jourdan, N., Longard, L., Biegel, T., & Metternich, J. (2021). Machine learning for intelligent maintenance and quality control: A review of existing datasets and corresponding use cases. Proceedings of the Conference on Production Systems and Logistics : CPSL 2021. https://doi.org/10.15488/11280
Kaur, K., Selway, M., Grossmann, G., Stumptner, M., & Johnston, A. (2018). Towards an open-standards based framework for achieving condition-based predictive maintenance. Proceedings of the 8th International Conference on the Internet of Things. https://doi.org/10.1145/3277593.3277608
Kaur, K., Selway, M., Grossmann, G., Stumptner, M., Johnston, A., & Wong, P. (2018). Towards an Open Standards-based Architecture for Condition-based Predictive Maintenance and IIoT. Semantic Web Journal, 46, 704-719.
Kelleher, J. D. (2019). Deep learning. MIT press.
Krishnamurthy, V., Nezafati, K., Stayton, E., & Singh, V. (2020). Explainable AI framework for imaging-based predictive maintenance for automotive applications and beyond. Data-Enabled Discovery and Applications, 4(1). https://doi.org/10.1007/s41688-020-00042-2
Kumar, P., Khalid, S., & Kim, H. S. (2023). Prognostics and health management of rotating machinery of industrial robot with deep learning applications – A review. Mathematics, 11(13), 3008. https://doi.org/10.3390/math11133008
Leukel, J., González, J., & Riekert, M. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Systems, 61, pp. 87-96. https://doi.org/10.1016/j.jmsy.2021.08.012
Levinski, O., Verhagen, W. J., Muscarello, V., Scott, M. J., Fayek, H. M., & Marzocca, P. (2023). An innovative high-fidelity approach to structural health monitoring. In AIAIC 2023: 20th Australian International Aerospace Congress (pp. 247-252). Engineers Australia.
Liu, Z., Hu, C., Jia, J., & Tao, F. (2021). Design of equipment condition maintenance knowledge base in power IOT based on edge computing. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). https://doi.org/10.1109/iaeac50856.2021.9390623
Lourenço, A., Ferraz, C., Meira, J., Marreiros, G., Bolón-Canedo, V., & Alonso-Betanzos, A. (2023). Automated green machine learning for condition-based maintenance. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, October 4-6, Bruges, Belgium. https://doi.org/10.14428/esann/2023.ES2023-85
Lu, H., Du, M., Qian, K., He, X., & Wang, K. (2022). GAN-based data augmentation strategy for sensor anomaly detection in industrial robots. IEEE Sensors Journal, 22(18), 17464-17474. https://doi.org/10.1109/JSEN.2021.3069452
Maheswari, R. U., & Umamaheswari, R. (2020). Wind turbine drivetrain expert fault detection system: Multivariate empirical mode decomposition based multi-sensor fusion with Bayesian learning classification. Intelligent Automation And Soft Computing, 26(3), 479-488. https://doi.org/10.32604/iasc.2020.013924
Martins, A. B., Fonesca, I., Farinha, J., Reis, J., & Cardoso, A. J. M. (2022). Prediction maintenance based of vibration analysis and deep learning – A case study of a drying press supported on a hidden Markov model. SSRN. http://doi.org/10.2139/ssrn.4194601
Maschler, B., Vietz, H., Jazdi, N., & Weyrich, M. (2020). Continual learning of fault prediction for turbofan engines using deep learning with elastic weight consolidation. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/ETFA46521.2020.9211903
Masmoudi, O., Jaoua, M., Jaoua, A., & Yacout, S. (2021). Data preparation in machine learning for condition-based maintenance. Journal of Computer Science, 17(6), 525–538. https://doi.org/10.3844/jcssp.2021.525.538
Nacchia, M., Fruggiero, F., Lambiase, A., & Bruton, K. (2021). A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Applied Sciences, 11(6), 2546. https://doi.org/10.3390/app11062546
Namuduri, S., Narayanan, B. N., Davuluru V. S. P., Burton, L., & Bhansali, S. (2020). Deep learning methods for sensor based predictive maintenance and future perspectives for electrochemical sensors. Journal of The Electrochemical Society, 167, 037552. https://doi.org/10.1149/1945-7111/ab67a8
Nejjar, I., Geissmann, F., Zhao, M., Taal, C., & Fink, O. (2024). Domain adaptation via alignment of operation profile for remaining useful lifetime prediction. Reliability Engineering & System Safety, 242, 109718. https://doi.org/10.1016/j.ress.2023.109718
Nemat Saberi, A., Belahcen, A., Sobra, J., & Vaimann, T. (2022). Lightgbm-based fault diagnosis of rotating machinery under changing working conditions using modified recursive feature elimination. IEEE Access, 10, 81910–81925. https://doi.org/10.1109/access.2022.3195939
Ngoma, W. J., Mativenga, P. T., & Pretorius, J. (2020) Enabling condition based maintenance in a precious metal processing plant. Procedia CIRP. 91, 893-898. https://doi.org/10.1016/j.procir.2020.04.138
Nor, A. A. M., Kassim, M., Minhat, M. S., & Ya’acob, N. (2022). A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality. International Journal of Electrical and Computer Engineering (IJECE), 12(6), pp. 6602-6613. https://10.11591/ijece.v12i6.pp6602-6613
Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in predictive maintenance – A review. CIRP Journal of Manufacturing Science and Technology, 40, 53-67. https://doi.org/10.1016/j.cirpj.2022.11.004
Ogunfowora, O. & Najjaran, H. (2023). Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization. Journal of Manufacturing Systems, 70, pp. 244-263. https://doi.org/10.1016/j.jmsy.2023.07.014
Payette, M. & Abdul-Nour, G. (2023) Machine learning applications for reliability engineering: A review. Sustainability, 15(7), 6270. https://doi.org/10.3390/su15076270
Polverino, L. et al. (2023). Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review. International Journal of Engineering Business Management, 15. https://doi.org/10.1177/18479790231186848
Quatrini, E., Constantino, F., Di Gravio, G., & Patriarca, R. (2020). Condition-based maintenance – An extensive literature review. Machines, 8(2), 31. https://doi.org/10.3390/machines8020031
Quispe G, D. A., Rajabiyazdi, F., & Jamieson, G. A. (2020). A machine learning-based Micro-World platform for condition-based maintenance. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/smc42975.2020.9283448
Ramuhalli, P., Huning, A., Guler Yigitoglu, A., & Saxena, A. (2023). Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML) (No. ORNL/SPR-2023/3072). Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States).
Rathore, M. S., & Harsha, S. P. (2022). Prognostic analysis of high-speed cylindrical roller bearing using Weibull distribution and k-nearest neighbor. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(1), 011005. https://doi.org/10.1115/1.4051314
Roux, M., Fang, Y. -P., & Barros, A. (2022). Maintenance planning under imperfect monitoring: An efficient POMDP model using interpolated value function. IFAC-PapersOnLine, 55(16), 128-135. https://doi.org/10.1016/j.ifacol.2022.09.012
Sanzana, M. R., Maul, T., Wong, J. Y., Abdulrazic, M. O. M., & Yip, C. -C. (2022). Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning. Automation in Construction, 141, 104445. https://doi.org/10.1016/j.autcon.2022.104445
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and Research Directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00592-x
Saurav, K., Avesh, M., Sharma, R. C., & Hossain, I. (2023). Deploying machine learning algorithms for predictive maintenance of high-value assets of Indian railways. Transportation Energy and Dynamics, pp. 401-426. https://doi.org/10.1007/978-981-99-2150-8_17
Schneider, J. & Vlachos, M. (2023). A survey of deep learning: From activations to transformers. arXiv:2302.00722. https://doi.org/10.48550/arXiv.2302.00722
Serradilla, O., Zugasti, E., Rodriguez, J., & Zurutuza, U. (2022). Deep learning models for predictive maintenance: A survey, comparison, challenges and prospects. Applied Intelligence, 52, pp. 10934-10964. https://doi.org/10.1007/s10489-021-03004-y
Sharma, J., Mittal, M. L., & Soni, G. (2022). Condition-based maintenance using machine learning and role of interpretability: A review. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-022-01843-7
Shin, M. -K. Woo, J. J., Cha, H. M., & Lee, S. -H. (2020). A study on the condition based maintenance evaluation system of smart plant device using convolutional neural network. Journal of Mechanical Science and Technology, 34, 2507-2514. https://doi.org/10.1007/s12206-020-0526-4
Siang, Y. Y., Ahamd, M. R., & Abidin, M. S. Z. (2021). Anomaly detection based on tiny machine learning: A review. Open International Journal of Informatics, 9(2), pp. 67-78. https://doi.org/10.11113/oiji2021.9nSpecial Issue 2.148
Singh, D. (2023). Internet of things. Factories of the Future: Technological Advancements in the Manufacturing Industry, 195–227. https://doi.org/10.1002/9781119865216.ch9
Singh, J., Azamfar, M., Li, F., & Lee, J. (2020). A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: Fundamentals, concepts and applications. Measurement Science and Technology, 32, 012001. https://doi.org/10.1088/1361-6501/ab8df9
Sinha, A., Pandaw, A. S., & Das, D. (2023). An intelligent fault detection framework for HVAC systems with alert generation. SN Computer Science, 4(5). https://doi.org/10.1007/s42979-023-02107-2
Sridhar, S., & Sanagavarapu, S. (2021). Handling data imbalance in predictive maintenance for machines using SMOTE-based oversampling. 2021 13th International Conference on Computational Intelligence and Communication Networks (CICN). https://doi.org/10.1109/cicn51697.2021.9574668
Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications, 221, 119738. https://doi.org/10.1016/j.eswa.2023.119738
Tanimoto, A. (2021). Combinatorial Q-learning for condition-based infrastructure maintenance. IEEE Access, 9, 46788–46799. https://doi.org/10.1109/access.2021.3059244
Tama, B. A., Vania, M., Lee, S., & Lim, S. (2023). Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals. Artificial Intelligence Review, 56, pp. 4667-4709. https://doi.org/10.1007/s10462-022-10293-3
Tessoni, V., & Amoretti, M. (2022). Advanced statistical and machine learning methods for multi-step multivariate time series forecasting in Predictive Maintenance. Procedia Computer Science, 200, 748–757. https://doi.org/10.1016/j.procs.2022.01.273
Teixeira, H. N., Lopes, I., & Braga, A. C. (2020). Condition-based maintenance implementation: A literature review. Procedia Manufacturing, 51, 228-235. https://doi.org/10.1016/j.promfg.2020.10.033
Timocin, T. (2020). Data quality in the interface of industrial manufacturing and machine learning (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-419983
Tran, M.-Q., Doan, H.-P., Vu, V. Q., & Vu, L. T. (2023). Machine learning and IOT-based approach for Tool Condition Monitoring: A review and future prospects. Measurement, 207, 112351. https://doi.org/10.1016/j.measurement.2022.112351
Tran, V. H., Lenssens, B., Kassab, A., Laks, A., Rivière, E., Rosinosky, G., & Sadre, R. (2022). Machine‐as‐a‐service: Blockchain‐based management and maintenance of industrial appliances. Engineering Reports, 5(7). https://doi.org/10.1002/eng2.12567
Tseremoglou, I. & Santos B.F. (2024). Condition-based maintenance scheduling of an aircraft fleet under partial observability: A deep reinforcement learning approach. Reliability Engineering & System Safety, 241, 109582. https://doi.org/10.1016/j.ress.2023.109582
van Staden, H. E., & Boute, R. N. (2021). The effect of multi-sensor data on condition-based maintenance policies. European Journal of Operational Research, 290(2), 585–600. https://doi.org/10.1016/j.ejor.2020.08.035
Wang, Q., Bu, S., & He, Z. (2020). Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Transactions on Industrial Informatics, 16(10), 6509-6517. https://doi.org/10.1109/TII.2020.2966033
Wang, Q., Liu, J., Wei, B., Chen, W., & Xu, S. (2020). Investigating the construction, training, and verification methods of K-means clustering fault recognition model for rotating machinery. IEEE Access, 8, 196515–196528. https://doi.org/10.1109/access.2020.3028146
Ward, T., Jenab, K., & Ortega-Moody, J. (2024). Machine learning models for condition-based maintenance with regular truncated signals. Decision Science Letters, 13(1), 197-210. https://doi.org/10.5267/j.dsl.2023.9.006
Xiong, J., Zhou, J., Ma, Y., Zhang, F., & Lin, C. (2023). Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns. Reliability Engineering & System Safety, 235, 109244. https://doi.org/10.1016/j.ress.2023.109244
Yang, J., Wang, X., and Luo, Z. (2024) Few-shot remaining useful life prediction based on meta-learning with deep sparse kernel network. Information Sciences, 653, 119795. https://doi.org/10.1016/j.ins.2023.119795
Yin, X., Liu, Q., Huang, X., & Pan, Y. (2022). Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning. Tunnelling and Underground Space Technology, 120, 104285. https://doi.org/10.1016/j.tust.2021.104285
Zenisek, J., Holzinger, F., & Affenzeller, M. (2019). Machine learning based concept drift detection for predictive maintenance. Computers & Industrial Engineering, 137, 106031. https://doi.org/10.1016/j.cie.2019.106031
Zhang, H., He, X., Yan, W., Jiang, Z., & Zhu, S. (2022). A machine learning-based approach for product maintenance prediction with reliability information conversion. Autonomous Intelligent Systems, 2(1). https://doi.org/10.1007/s43684-022-00033-3
Zhang, N., & Si, W. (2020). Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks. Reliability Engineering & System Safety, 203, 107094. https://doi.org/10.1016/j.ress.2020.107094
Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X. (2020). Federated learning for machinery fault diagnosis with dynamic validation and self-supervision. Knowledge-Based Systems, 213, 106679, https://doi.org/10.1016/j.knosys.2020.106679
Zhao, Z., Li, T., Wu, J., Sun, C., Wang, S., Yan, R., & Chen, X. (2020). Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Transactions, 107, 224–255. https://doi.org/10.1016/j.isatra.2020.08.010
Zhu, M., & Zhou, X. (2023). Hierarchical-clustering-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems. International Journal of Production Economics, 264, 108971. https://doi.org/10.1016/j.ijpe.2023.108971
Zschech, P., Heinrich, K., Bink, R., & Neufeld, J. S. (2019). Prognostic model development with missing labels: A condition-based maintenance approach using machine learning. Business & Information Systems Engineering, 61(3), 327–343. https://doi.org/10.1007/s12599-019-00596-1
Section
Technical Papers