Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning
Source abstract
Abstract
Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Quality and Safety in Healthcare · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
References (52)
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1. Giusto, D., Iera, A., Morabito, G., and Atzori, L. (2010). The Internet of Things: 20th Tyrrhenian Workshop on Digital Communications, Springer.10.1007/978-1-4419-1674-7 ↗
2. SmartFactory—Towards a factory-of-things10.1016/j.arcontrol.2010.02.008 ↗
3. Dragomir, O., Gouriveau, R., Dragomir, F., Minca, E., and Zerhouni, N. (2009). 2009 European Control Conference (ECC), IEEE.
4. Mobley, R. (2002). Plant Engineer’s Handbook, Elsevier.
5. Gertsbakh, I.B. (1977). Gertsbakh: Models of Preventive Maintenance, Elsevier.
6. Current status of machine prognostics in condition-based maintenance: A review10.1007/s00170-009-2482-0 ↗
7. Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., and Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Wiley.10.1002/9780470117842 ↗
8. Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., and Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Appl. Sci., 12.10.3390/app12168081 ↗
9. Aliyu, R., Mokhtar, A.A., and Hussin, H. (2022). Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review. Appl. Sci., 12.10.3390/app122211691 ↗
10. Industrial maintenance decision-making: A systematic literature review10.1016/j.jmsy.2017.09.003 ↗
11. Peron, M., Fragapane, G., Sgarbossa, F., and Kay, M. (2020). Digital Facility Layout Planning. Sustainability, 12.10.3390/su12083349 ↗
12. Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing
13. Jamwal, A., Agrawal, R., Sharma, M., and Giallanza, A. (2021). Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions. Appl. Sci., 11.10.3390/app11125725 ↗
14. Miśkiewicz, R., and Wolniak, R. (2020). Practical Application of the Industry 4.0 Concept in a Steel Company. Sustainability, 12.10.3390/su12145776 ↗
15. Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., and Zahariadis, T. (2019). Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects. Sensors, 20.10.3390/s20010109 ↗
16. A predictive model for the maintenance of industrial machinery in the context of industry 4.010.1016/j.engappai.2019.103289 ↗
17. Kagermann, H., and Reinhart, J. (2013). Fortschritte im Rechnungswesen, Gabler Verlag.
18. Industrie 4.0—Volkswirtschaftliches Potenzial für Deutschland10.15358/0935-0381-2015-8-9-515 ↗
19. Edge Computing Architectures in Industry 4.0: A General Survey and Comparison
20. Internet of Services10.1007/s12599-009-0066-z ↗
21. Lucke, D., Constantinescu, C., and Westkämper, E. (2008). Manufacturing Systems and Technologies for the New Frontier, Springer.
22. A review of machine learning for the optimization of production processes10.1007/s00170-019-03988-5 ↗
23. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems10.1016/j.mfglet.2014.12.001 ↗
24. Intelligent prognostics tools and e-maintenance10.1016/j.compind.2006.02.014 ↗
25. Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance; Service Innovation10.1016/j.procir.2015.08.026 ↗
26. Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment10.1016/j.procir.2014.02.001 ↗
27. Athulan, V., Will, S., Armando, F., David, D., and Paul, W. (2008). Improving Machine Tool Interoperability Using Standardized Interface Protocols: MT Connect. Laboratory for Manufacturing and Sustainability, ASME Publisher.
28. An electrical DC Motor Equivalent Circuit testbed for the battery Prognostic Health and Management
29. A predictive maintenance cost model for CNC SMEs in the era of Industry 4.010.1007/s00170-019-04094-2 ↗
30. A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms10.1016/j.ifacol.2019.10.024 ↗
31. Dvorzak, M., Magnien, J., Kleb, U., Kraker, E., and Mücke, M. (2022). Bayesian Hierarchical Modelling for Uncertainty Quantification in Operational Thermal Resistance of LED Systems. Appl. Sci., 12.10.3390/app121910063 ↗
32. Sadoughi, M., Lu, H., and Hu, C. (2019, January 17–20). A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings. Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, CA, USA.10.1109/ICPHM.2019.8819442 ↗
33. A neural network filtering approach for similarity-based remaining useful life estimation10.1007/s00170-018-2874-0 ↗
34. Multi-Scale Volatility Feature Analysis and Prediction of Gold Price10.1142/S0219622016500504 ↗
35. A.; Fraile, J.; Chamoso, P.; González-Briones, A.; Sittón, I.; Corchado, J. A Predictive Maintenance Model Using Recurrent Neural Networks10.1007/978-3-030-20055-8_25 ↗
36. Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery10.1115/1.2815585 ↗
37. Jakkamputi, L., Devaraj, S., Marikkannan, S., Gnanasekaran, S., Ramasamy, S., Rakkiyannan, J., and Xu, Y. (2022). Experimental and Computational Vibration Analysis for Diagnosing the Defects in High Performance Composite Structures Using Machine Learning Approach. Appl. Sci., 12.10.3390/app122312100 ↗
38. Machine Health Monitoring and Life Management Using Finite-Element-Based Neural Networks10.1115/1.2817002 ↗
39. Diagnostic rule extraction from trained feedforward neural networks10.1006/mssp.2001.1396 ↗
40. Battineni, G., Sagaro, G.G., Nalini, C., Amenta, F., and Tayebati, S.K. (2019). Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods. Machines, 7.10.3390/machines7040074 ↗
41. Machine component health prognostics with only truncated histories using geometrical metric approach10.1016/j.ymssp.2017.01.052 ↗
42. Larson, E., Wipf, D., and Parker, B. (1997, January 19–22). Gear and bearing diagnostics using neural network-based amplitude and phase demodulation. Proceedings of the 51st Meeting of the Society for Machinery Failure Prevention Technology, New York, NY, USA.
43. Neural-network-based motor rolling bearing fault diagnosis10.1109/41.873214 ↗
44. A Neural Network Degradation Model for Computing and Updating Residual Life Distributions10.1109/TASE.2007.910302 ↗
45. Bhavsar, K., Vakharia, V., Chaudhari, R., Vora, J., Pimenov, D.Y., and Giasin, K. (2022). A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models. Machines, 10.10.3390/machines10030176 ↗
46. Dong, D., Hopfield, J., and Unnikrishnan, K. (1997, January 24–26). Neural networks for engine fault diagnostics. Proceedings of the Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, Amelia Island, FL, USA.
47. Hybrid prognostic method applied to mechatronic systems10.1007/s00170-013-5064-0 ↗
48. A prognostic algorithm for machine performance assessment and its application10.1080/09537280412331309208 ↗
49. Application of relevance vector machine and logistic regression for machine degradation assessment10.1016/j.ymssp.2009.10.011 ↗
50. Czepiel, S.A. (2022, December 21). Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation. Available online: https://czep.net/stat/mlelr.pdf.
51. Temporal variations of reference evapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China10.1016/j.wse.2015.01.004 ↗
52. Neural-network-based d-step-ahead predictors for nonlinear systems with time delay10.1016/S0952-1976(98)00043-8 ↗