Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge

References

• Able, C. M., A. H. Baydush, C. Nguyen, J. Gersh, A. Ndlovu, I. Rebo, J. Booth, M. Perez, B. Sintay, M. T. Munley, et al. 2016. “A Model for Preemptive Maintenance of Medical Linear Accelerators-predictive Maintenance.” Radiation Oncology 11 (1): 36. doi:10.1186/s13014-016-0602-1.
   PubMedGoogle Scholar
• Aivaliotis, P., K. Georgoulias, and G. Chryssolouris. 2019. “The Use of Digital Twin for Predictive Maintenance in Manufacturing.” International Journal of Computer Integrated Manufacturing 32 (11): 1067–1080. doi:10.1080/0951192X.2019.1686173.
   Web of Science ®Google Scholar
• Albano, M., E. Jantunen, G. Papa, and U. Zurutuza. 2018. “The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance.“ In Albano, Michele, Jantunen, Erkki, Papa, Gregor, and Zurutuza, Urko The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance. River Publishers 1–500 9788793609853. doi:10.13052/rp-9788793609846.
   Google Scholar
• Alexopoulos, K., N. Nikolakis, and G. Chryssolouris. 2020. “Digital Twin-driven Supervised Machine Learning for the Development of Artificial Intelligence Applications in Manufacturing.“ International Journal of Computer Integrated Manufacturing, 33(5): 429–439. doi:10.1080/0951192X.2020.1747642.
   Web of Science ®Google Scholar
• Almaged, M., and J. Hale. 2019. “Virtual Instruments Based Approach to Vibration Monitoring, Processing and Analysis.” International Journal of Instrumentation and Measurement, 4, 9–16.
   Google Scholar
• Alvares, A. J., and R. Gudwin. 2019. “Integrated System of Predictive Maintenance and Operation of Eletronorte Based on Expert System.” IEEE Latin America Transactions 17 (1): 155–166. doi:10.1109/TLA.2019.8826707.
   Web of Science ®Google Scholar
• Andersen, B., and T. Fagerhaug. 2006. Root Cause Analysis: Simplified Tools and Techniques. Norway: Quality Press.
   Google Scholar
• Ansari, F., R. Glawar, and W. Sihn. 2020. “Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks.” In Machine Learning for Cyber Physical Systems, edited by J. Beyerer, A. Maier, and O. Niggemann, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-662-59084-3_1.
   Google Scholar
• Bergquist, B., and P. Söderholm. 2016. “Predictive Modelling for Estimation of Railway Track Degradation.” In Current Trends in Reliability, Availability, Maintainability and Safety, edited by U. Kumar, A. Ahmadi, A. K. Verma, and P. Varde, 331–347. Cham: Springer International Publishing.
   Google Scholar
• Borodulin, K., L. Sokolinsky, G. Radchenko, A. Tchernykh, A. Shestakov, and R. Prodan. 2017. “Towards Digital Twins Cloud Platform: Microservices and Computational Workflows to Rule a Smart Factory.” In UCC 2017 - Proceedings of the10th International Conference on Utility and Cloud Computing Austin Texas USA, 205–206. doi:10.1145/3147213.3149234.
   Google Scholar
• Bousdekis, A., B. Magoutas, D. Apostolou, and G. Mentzas. 2015. “A Proactive Decision Making Framework for Condition-based Maintenance.” Industrial Management & Data Systems 115 (7): 1225–1250. doi:10.1108/IMDS-03-2015-0071.
   Web of Science ®Google Scholar
• Brownlee, J. 2018. Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict the Future. Texas USA: Machine Learning Mastery.
   Google Scholar
• Cao, Q., C. Zanni-Merk, A. Samet, F. D. B. de Beuvron, and C. Reich. 2020. “Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems.” Cybernetics and Systems 51 (2): 161–176. doi:10.1080/01969722.2019.1705550.
   Web of Science ®Google Scholar
• Carvalho, T. P., F. A. A. M. N. Soares, R. Vita, R. D. P. Francisco, J. P. Basto, and S. G. S. Alcalá. 2019. “A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance.” Computers & Industrial Engineering 137: 106024. doi:10.1016/j.cie.2019.106024.
   Web of Science ®Google Scholar
• Cerqueira, V., F. Pinto, C. Sá, and C. Soares. 2016. “Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance.” In H. Boström, A. Knobbe, C. Soares, and P. Papapetrou, edited by. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Vol. 9897 LNCS pp. 393–397). Cham: Springer International Publishing. doi:10.1007/978-3-319-46349-0_35.
   Google Scholar
• Chapman, P., J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth. 2000. CRISP-DM 1.0: Cross Industry Standard Process for Data Mining. CRISP-DM Consortium.
   Google Scholar
• Coleman, C., S. Damodaran, M. Chandramouli, and E. Deuel. 2017. “Industry 4.0 And Predictive Technologies for Asset Maintenance.” Deloitte Insights. Accessed 16 September 2019. https://www2.deloitte.com/us/en/insights/focus/industry-4-0/using-predictive-technologies-for-asset-maintenance.html
   Google Scholar
• Dhillon, B. S. 2002. Engineering Maintenance: A Modern Approach. Florida USA: cRc press.
   Google Scholar
• Diez-Olivan, A., J. A. Pagan, R. Sanz, and B. Sierra. 2017. “Data-driven Prognostics Using a Combination of Constrained K-means Clustering, Fuzzy Modeling and LOF-based Score.” Neurocomputing 241: 97–107. doi:10.1016/j.neucom.2017.02.024.
   Web of Science ®Google Scholar
• Dos Santos, T., F. J. T. E. Ferreira, J. M. Pires, and C. Damasio. 2017. “Stator Winding Short-circuit Fault Diagnosis in Induction Motors Using Random Forest.” In 2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017, Miami, FL, USA, 1–8. doi:10.1109/IEMDC.2017.8002350.
   Google Scholar
• Frank, A. G., L. S. Dalenogare, and N. F. Ayala. 2019. “Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies.” International Journal of Production Economics 210: 15–26. doi:10.1016/J.IJPE.2019.01.004.
   Web of Science ®Google Scholar
• Guzman, A., and H. Leonardo Rodriguez Borja. 2014. “Análisis De Modos Y Efectos De Falla Para Mejorar La Disponibilidad Operacional En La Línea De Producción De Gaseosas, 81.” https://repository.unilibre.edu.co/bitstream/handle/10901/7838/Doc Final Proyecto A-rmando y Leonardo sustentacion.pdf?sequence=1
   Google Scholar
• Institute’, ’Deloitte Analytics. 2017. Predictive Maintenance: Taking Pro-active Measures Based on Advanced Data Analytics to Predict and Avoid Machine Failure. Birmingham, AL, USA: Deloitte Consulting GmbH.
   Google Scholar
• ISO 17359. 2018. Condition Monitoring and Diagnostics of Machines — General Guidelines. Vol. 2018. Geneva, Switzerland: pub-ISO:adr.
   Google Scholar
• Jordan, W. E. 1972. “Failure Modes, Effects and Criticality Analyses.”
   Google Scholar
• Khan, S., and T. Yairi. 2018. “A Review on the Application of Deep Learning in System Health Management.” Mechanical Systems and Signal Processing 107: 241–265. doi:10.1016/j.ymssp.2017.11.024.
   Web of Science ®Google Scholar
• Lavi, Y. 2018. “The Rewards and Challenges of Predictive Maintenance.” InfoQ. https://www.infoq.com/articles/predictive-maintenance-industrial-iot/
   Google Scholar
• Lebold, M., K. Reichard, C. S. Byington, and R. Orsagh. 2002. “OSA-CBM Architecture Development with Emphasis on XML Implementations.” In Maintenance and Reliability Conference (MARCON) Tennessee USA, 6–8. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.4066&rep=rep1&type=pdf
   Google Scholar
• Ley, C. P., and M. E. Orchard. 2021. “Simultaneous Inference of Lithium-Ion Battery Polarising Impedance Surface and Capacity Degradation Using a Hybrid Neural Adaptive State Space Model.” Journal of Energy Storage 36: 102370. doi:10.1016/J.EST.2021.102370.
   Web of Science ®Google Scholar
• Li, D., and J. Gao. 2010. “Study and Application of Reliability-centered Maintenance considering Radical Maintenance.” Journal of Loss Prevention in the Process Industries 23 (5): 622–629. doi:10.1016/j.jlp.2010.06.008.
   Web of Science ®Google Scholar
• Li, Z., and Y. Wang. 2018. “Domain Knowledge in Predictive Maintenance for Water Pipe Failures.” In Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, edited by J. Zhou and F. Chen, 437–457. Cham: Springer International Publishing. doi:10.1007/978-3-319-90403-0_21.
   Google Scholar
• Liao, L., and F. Köttig. 2016. “A Hybrid Framework Combining Data-driven and Model-based Methods for System Remaining Useful Life Prediction.” Applied Soft Computing Journal 44: 191–199. doi:10.1016/j.asoc.2016.03.013.
   Web of Science ®Google Scholar
• Lukac, D. 2016. “The Fourth ICT-based Industrial Revolution “Industry 4.0” - HMI and the Case of CAE/CAD Innovation with EPLAN P8.” 2015 23rd Telecommunications Forum, TELFOR 2015 835–838. doi:10.1109/TELFOR.2015.7377595.
   Google Scholar
• Malhotra, P., V. Tv, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff. 2016. Multi-Sensor Prognostics Using an Unsupervised Health Index Based on LSTM Encoder-Decoder 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management San Francisco, CA, USA 10 pages.
   Google Scholar
• Mishali, M., and Y. C. Eldar. 2009. “Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals.” IEEE Transactions on Signal Processing 57 (3): 993–1009. doi:10.1109/TSP.2009.2012791.
   Web of Science ®Google Scholar
• Nuñez, D. L., and M. Borsato. 2018. “OntoProg: An Ontology-based Model for Implementing Prognostics Health Management in Mechanical Machines.” Advanced Engineering Informatics 38: 746–759. doi:10.1016/j.aei.2018.10.006.
   Web of Science ®Google Scholar
• O’Donovan, P., K. Leahy, K. Bruton, and D. T. J. O’Sullivan. 2015. “An Industrial Big Data Pipeline for Data-driven Analytics Maintenance Applications in Large-scale Smart Manufacturing Facilities.” Journal of Big Data 2 (1): 1–26. doi:10.1186/S40537-015-0034-Z.
   Google Scholar
• Okoh, C., R. Roy, and J. Mehnen. 2017. “Predictive Maintenance Modelling for Through-Life Engineering Services.” Procedia CIRP 59: 196–201. doi:10.1016/j.procir.2016.09.033.
   Google Scholar
• Prajapati, A., J. Bechtel, and S. Ganesan. 2012. “Condition Based Maintenance: A Survey.” Journal of Quality in Maintenance Engineering 18 (4): 384–400. doi:10.1108/13552511211281552.
   Google Scholar
• Rana, A., A. K. Verma, and A. Srividya. 2016. “Maintenance of Large Engineering Systems.” In Current Trends in Reliability, Availability, Maintainability and Safety, edited by U. Kumar, A. Ahmadi, A. K. Verma, and P. Varde, 599–609. Cham: Springer International Publishing.
   Google Scholar
• Ransbotham, S., D. Kiron, P. Gerbert, and M. Reeves. 2017. “Reshaping Business With Artificial Intelligence.” MIT Sloan Mangement Review and the Boston Consulting Group 59181: 1–17. doi:10.3934/dcdsb.2008.10.925.
   Google Scholar
• Reliasoft. 2007. “Basic Steps of Applying Reliability Centered Maintenance (RCM).” Reliability HotWire. Accessed 29 September 2020. https://www.weibull.com/hotwire/issue76/relbasics76.htm
   Google Scholar
• Rieger, T., S. Regier, I. Stengel, and N. Clarke. 2019. “Fast Predictive Maintenance in Industrial Internet of Things (Iiot) with Deep Learning (DL): A Review.” In CEUR Workshop Proceedings, Darmstadt, Germany, 2348, 69–79.
   Google Scholar
• Saxena, A., and K. Goebel. 2008. “Turbofan Engine Degradation Simulation Data Set.” NASA Ames Prognostics Data Repository 878–887.
   Google Scholar
• Selcuk, S. 2017. “Predictive Maintenance, Its Implementation and Latest Trends.” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231 (9): 1670–1679. doi:10.1177/0954405415601640.
   Web of Science ®Google Scholar
• Serradilla, O., E. Zugasti, C. Cernuda, A. Aranburu, J. R. de Okariz, and U. Zurutuza. 2020. “Interpreting Remaining Useful Life Estimations Combining Explainable Artificial Intelligence and Domain Knowledge in Industrial Machinery.” In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, Scotland, UK, IEEE. 1–8.
   Google Scholar
• Serradilla, O., E. Zugasti, J. R. D. Okariz, J. Rodriguez, and U. Zurutuza. 2021. “Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data.” Applied Sciences 11 (16): 7376. doi:10.3390/APP11167376.
   Google Scholar
• Serradilla, O., E. Zugasti, Rodriguez, J., and U. Zurutuza. 2022. “Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects Applied Intelligence 1–31 doi:10.1007/s10489-021-03004-y .”
   Web of Science ®Google Scholar
• UESystems. 2019. “Understanding the P-F Curve and Its Impact on Reliability Centered Maintenance.” July. Accessed 18 September 2019. http://www.uesystems.com/news/understanding-the-p-f-curve-and-its-impact-on-reliability-centered-maintenance
   Google Scholar
• UNE-EN 13306. 2018. Maintenance. Maintenance Terminology. Vol. 2018. Génova, Madrid: AENOR.
   Google Scholar
• Vaidya, S., P. Ambad, and S. Bhosle. 2018. “Industry 4.0 – A Glimpse.” Procedia Manufacturing 20: 233–238. doi:10.1016/J.PROMFG.2018.02.034.
   Google Scholar
• Venkatasubramanian, V., R. Rengaswamy, K. Yin, and S. N. Kavuri. 2003. “A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-based Methods.” Computers & Chemical Engineering 27 (3): 293–311. doi:10.1016/S0098-1354(02)00160-6.
   Web of Science ®Google Scholar
• Vlasov, A. I., V. V. Echeistov, A. I. Krivoshein, V. A. Shakhnov, S. S. Filin, and V. S. Migalin. 2018. “An Information System of Predictive Maintenance Analytical Support of Industrial Equipment.” Journal of Applied Engineering Science 16 (4): 515–522. doi:10.5937/jaes16-18405.
   Google Scholar
• Welz, Z. A. 2017. Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance. Modeling : Maintenance-Based Prognostics for Long-Term Equipment Operation.
   Google Scholar
• Wiseman, M. 2006. “A History of CBM (Condition Based Maintenance).” omdec.com. Accessed 17 September 2019. http://www.omdec.com/moxie/Technical/Reliability/a-history-of-cbm.shtml
   Google Scholar
• Yuan, M., Y. Wu, and L. Lin. “2016. “Fault Diagnosis and Remaining Useful Life Estimation of Aero Engine Using LSTM Neural Network.” In AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems Beijing, China, 135–140. doi:10.1109/AUS.2016.7748035.
   Google Scholar
• Zhang, X., R. Xu, C. Kwan, S. Y. Liang, Q. Xie, and L. Haynes. 2005. “An Integrated Approach to Bearing Fault Diagnostics and Prognostics.” In Proceedings of the American Control Conference Portland, Or, USA, 4. 2750–2755. doi:10.1109/acc.2005.1470385.
   Google Scholar
• Zhang, W., D. Yang, and H. Wang. 2019. “Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey.” IEEE Systems Journal 13 (3): 2213–2227. doi:10.1109/JSYST.2019.2905565.
   Web of Science ®Google Scholar
• Zhao, H., and F. Magoulès. 2012. “A Review on the Prediction of Building Energy Consumption.” Renewable and Sustainable Energy Reviews 16 (6): 3586–3592. doi:10.1016/j.rser.2012.02.049.
   Web of Science ®Google Scholar
• Zhao, F., Z. Tian, and Y. Zeng. 2013. “Uncertainty Quantification in Gear Remaining Useful Life Prediction through an Integrated Prognostics Method.” IEEE Transactions on Reliability 62 (1): 146–159. doi:10.1109/TR.2013.2241216.
   Web of Science ®Google Scholar
• Zhou, X., L. Xi, and J. Lee. 2007. “Reliability-centered Predictive Maintenance Scheduling for a Continuously Monitored System Subject to Degradation.” Reliability Engineering & System Safety 92 (4): 530–534. doi:10.1016/j.ress.2006.01.006.
   Web of Science ®Google Scholar