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Quality Quandaries

A primer on predictive maintenance: Potential benefits and practical challenges

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References

  • Alharbi, B., Z. Liang, J. M. Aljindan, A. K. Agnia, and X. Zhang. 2022. Explainable and interpretable anomaly detection models for production data. SPE Journal 27 (1):349–63. doi: 10.2118/208586-PA.
  • Betti, A., E. Crisostomi, G. Paolinelli, A. Piazzi, F. Ruffini, and M. Tucci. 2021. Condition monitoring and predictive maintenance methodologies for hydropower plants equipment. Renewable Energy 171:246–53. doi: 10.2118/208586-PA.
  • Boston Consulting Group. 2023. BCG-WEF project: AI-powered industrial operations. BCG Global. https://www.bcg.com/about/partner-ecosystem/world-economic-forum/ai-project-survey
  • Bousdekis, A., K. Lepenioti, D. Apostolou, and G. Mentzas. 2021. A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics 10 (7):828–47. doi: 10.3390/electronics10070828.
  • Cacciarelli, D., and M. Kulahci. 2022. A novel fault detection and diagnosis approach based on orthogonal autoencoders. Computers & Chemical Engineering 163:107853. doi: 10.1016/j.compchemeng.2022.107853.
  • Cacciarelli, D., and M. Kulahci. 2023. Hidden dimensions of the data: PCA vs autoencoders. Quality Engineering 35 (4):741–50. doi: 10.1080/08982112.2023.2231064.
  • Chen, N., and K. L. Tsui. 2013. Condition monitoring and remaining useful life prediction using degradation signals: Revisited. IIE Transactions 45 (9):939–52. doi: 10.1080/0740817X.2012.706376.
  • Chen, Z., C. K. Yeo, B. S. Lee, and C. T. Lau. 2018. Autoencoder-based network anomaly detection. 2018 Wireless Telecommunications Symposium (WTS), 1–5. doi: 10.1109/WTS.2018.8363930.
  • Ciancio, V., L. Homri, J.-Y. Dantan, and A. Siadat. 2023. Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context. International Journal of Production Research 62 (6):2255–71. doi: 10.1080/00207543.2023.2217293.
  • Ferrer, A. 2013. Latent structures-based multivariate statistical process control: A paradigm shift. Quality Engineering 26 (1):72–91. doi: 10.1080/08982112.2013.846093.
  • Frumosu, F. D., G. Ørnskov Rønsch, and M. Kulahci. 2020. Mould wear-out prediction in the plastic injection moulding industry: A case study. International Journal of Computer Integrated Manufacturing 33 (12):1245–58. doi: 10.1080/0951192X.2020.1829062.
  • Hansen, H. H., M. Kulahci, and B. F. Nielsen. 2023. Statistical process control versus deep learning for power plant condition monitoring. Computers & Chemical Engineering 178:108391. doi: 10.1016/j.compchemeng.2023.108391.
  • Hansen, H. H., N. MacDougall, C. D. Jensen, M. Kulahci, and B. F. Nielsen. 2024. Condition monitoring of wind turbine faults: Modelling and savings. Applied Mathematical Modelling 130:160–74. doi: 10.1016/j.apm.2024.02.036.
  • Hermansa, M., M. Kozielski, M. Michalak, K. Szczyrba, Ł. Wróbel, and M. Sikora. 2021. Sensor-based predictive maintenance with reduction of false alarms—A case study in heavy industry. Sensors 22 (1):226. doi: 10.3390/s22010226.
  • Isermann, R. 2011. Fault-diagnosis applications. Berlin; Heidelberg: Springer.
  • Krupitzer, C., T. Wagenhals, M. Zufle, V. Lesch, D. Shafer, A. Mozaffarin, J. Edinger, C. Becker, and S. Kounev. 2020. A survey on predictive maintenance for Industry 4.0. arXiv Preprint arXiv:2002.08224.
  • Lipton, Z. C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16 (3):31–57. doi: 10.1145/3236386.3241340.
  • Liu, F. T., K. M. Ting, and Z. Zhou. 2008. Isolation forest. 8th IEEE International Conference on Data Mining, 413–22.
  • Mihai, S., W. Davis, D. V. Hung, R. Trestian, M. Karamanoglu, B. Barn, R. V. Prasad, H. Venkataraman, and H. Nguyen. 2021. A digital twin framework for predictive maintenance in industry 4.0. 2020 International Conference on High Performance Computing & Simulation.
  • Miller, T. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1–38. doi: 10.1016/j.artint.2018.07.007.
  • Mobley, R. K. 2013. An introduction to predictive maintenance. 2nd ed. Oxford, UK: Butterworth-Heinemann.
  • Montgomery, D. 2020. Introduction to statistical quality control. 7th ed. New York, NY: John Wiley & Sons.
  • Najafabadi, M. M., F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic. 2015. Deep learning applications and challenges in big data analytics. Journal of Big Data 2 (1):21. doi: 10.1186/s40537-014-0007-7.
  • Pang, G., C. Shen, and A. van den Hengel. 2019. Deep anomaly detection with deviation networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi: 10.1145/3292500.3330871.
  • Schulze, J. P., P. Sperl, and K. Bottinger. 2021. Anomaly detection by recombining gated unsupervised experts. arXiv. sciencedirect.com/science/article/pii/S0098135423002612.
  • Sedghi, M., B. Bergquist, E. Vanhatalo, and A. Migdalas. 2022. Data-driven maintenance planning and scheduling based on predicted railway track condition. Quality and Reliability Engineering International 38 (7):3689–709. doi: 10.1002/qre.3166.
  • van Dinter, R., B. Tekinerdogan, and C. Catal. 2022. Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology 151:107008. doi: 10.1016/j.infsof.2022.107008.
  • Zong, B., Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen. 2018. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. ICLR 2018 Conference.

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