journal article·2022-04-21Open access

Smart Manufacturing in Rolling Process Based on Thermal Safety Monitoring by Fiber Optics Sensors Equipping Mill Bearings

Eugenio Brusa, Cristiana Delprete, Lorenzo Giorio

Applied Sciences· Volume 12· Issue 9

Abstract

The steel rolling process is critical for safety and maintenance because of loading and thermal operating conditions. Machinery condition monitoring (MCM) increases the system’s safety, preventing the risk of fire, failure, and rupture. Equipping the mill bearings with sensors allows monitoring of the system in service and controls the heating of mill components. Fiber optic sensors detect loading condition, vibration, and irregular heating. In several systems, access to machinery is rather limited. Therefore, this paper preliminarily investigates how fiber optics can be effectively embedded within the mill cage to set up a smart manufacturing system. The fiber Bragg gratings (FBG) technology allows embedding sensors inside the pins of backup bearings and performing some prognosis and diagnosis activities. The study starts from the rolling mill layout and defines its accessibility, considering some real industrial cases. Testing of an FBG sensor prototype checks thermal monitoring capability inside a closed cavity, obtained on the surface of either the fixed pin of the backup bearing or the stator surrounding the outer ring. Results encourage the development of the whole prototype of the MCM system to be tested on a real mill cage in full operation.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Fiber Optic Sensors · Advanced machining processes and optimization · Advanced Measurement and Metrology Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy

References (71)

Show source-provided references
1. Rentz, O., Jochum, R., and Schultmann, F. (1999). Report on Best Available Techniques (BAT) in the German Ferrous Metals Processing Industry, Deutsch-Französisches Institut für Umweltforschung (DFIU).
2. Numerical and Experimental Analysis of the Dynamic Effects in Compact Cluster Mills for Cold Rolling10.1016/j.jmatprotec.2008.05.044 ↗
3. Trends in the Steel Industry: Innovative Solutions to Drive Sustainability10.1007/s12666-013-0298-y ↗
4. Verma, N.K., and Salour, A. (2020). Intelligent Condition Based Monitoring: For Turbines, Compressors, and Other Rotating Machines, Springer. Studies in Systems, Decision and Control.10.1007/978-981-15-0512-6 ↗
5. Brunton, S.L., and Kutz, J.N. (2019). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press. [1st ed.].10.1017/9781108380690 ↗
6. Brusa, E. (2017, January 11–13). Development of a Sentry Smart Bearing as a Node for Connectivity and Monitoring of Steelmaking System. Proceedings of the 2017 IEEE International Systems Engineering Symposium (ISSE), Vienna, Austria.10.1109/SysEng.2017.8088257 ↗
7. Brusa, E., Bruzzone, F., Delprete, C., Di Maggio, L.G., and Rosso, C. (2020). Health Indicators Construction for Damage Level Assessment in Bearing Diagnostics: A Proposal of an Energetic Approach Based on Envelope Analysis. Appl. Sci., 10.10.3390/app10228131 ↗
8. Galar, D., and Kumar, U. (2013, January 6–7). SMART Bearings: From Sensing to Actuation. Proceedings of the 12th IMEKO TC10 Workshop on Technical Diagnostics, Florence, Italy.
9. Shao, Y., Ge, L., and Fang, J. (2008, January 14–17). Fault Diagnosis System Based on Smart Bearing. Proceedings of the 2008 International Conference on Control, Automation and Systems, Seoul, Korea.10.1109/ICCAS.2008.4694313 ↗
10. Mohanty, A.R. (2017). Machinery Condition Monitoring: Principles and Practices, CRC Press.
11. Randall, R.B. (2011). Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications, John Wiley & Sons, Ltd.10.1002/9780470977668 ↗
12. Elangovan, U. (2019). Smart Automation to Smart Manufacturing: Industrial Internet of Things, Momentum Press.
13. Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis10.1016/j.matpr.2021.05.447 ↗
14. Cusano, A., Cutolo, A., and Albert, J. (2011). Fiber Bragg Grating Sensors: Recent Advancements, Industrial Applications and Market Exploitation, Bentham Science.10.2174/97816080508401110101 ↗
15. Design and Structural Optimization of the Electric Arc Furnace Through a Mechatronic-Integrated Modeling Activity10.1109/TMECH.2014.2364392 ↗
16. Smart Machines with Flexible Rotors10.1016/j.ymssp.2010.09.006 ↗
17. Genta, G. (2005). Dynamics of Rotating Systems, Springer.10.1007/0-387-28687-X ↗
18. Some Considerations on the Basic Assumptions in Rotordynamics10.1006/jsvi.1999.2354 ↗
19. Design of a Kinematic Vibration Energy Harvester for a Smart Bearing with Piezoelectric/Magnetic Coupling10.1080/15376494.2018.1508795 ↗
20. Miśkiewicz, R., and Wolniak, R. (2020). Practical Application of the Industry 4.0 Concept in a Steel Company. Sustainability, 12.10.3390/su12145776 ↗
21. Neef, C., Hirzel, S., and Arens, M. (2018). Industry 4.0 in the European Iron and Steel Industry: Towards an Overview of Implementations and Perspectives, Fraunhofer Institute for Systems and Innovation Research (ISI).
22. Digital Twin: Towards the Integration Between System Design and RAMS Assessment Through the Model-Based Systems Engineering10.1109/JSYST.2020.3010379 ↗
23. Vibration Analysis of a Sensor-Integrated Ball Bearing10.1115/1.1285943 ↗
24. Kovacs, A., Peroulis, D., and Sadeghi, F. (2007, January 28–31). Early-Warning Wireless Telemeter for Harsh-Environment Bearings. Proceedings of the 2007 IEEE Sensors, Atlanta, GA, USA.10.1109/ICSENS.2007.4388559 ↗
25. Draney, R.K. (2008, January 1–8). High Temperature Sensor for Bearing Health Monitoring. Proceedings of the 2008 IEEE Aerospace Conference, Big Sky, MT, USA.10.1109/AERO.2008.4526602 ↗
26. Wireless Sensor Node Powered by Aircraft Specific Thermoelectric Energy Harvesting10.1016/j.sna.2010.12.020 ↗
27. A Smart Sensing Unit for Vibration Measurement and Monitoring10.1109/TMECH.2009.2016956 ↗
28. Winkelmann, C., Woitschach, O., Meyer, E., and Lang, W. (2011, January 5–9). Development of a Strain Sensor for Rolling Contact Loads. Proceedings of the 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference, Beijing, China.10.1109/TRANSDUCERS.2011.5969179 ↗
29. Bashir, I., Wang, L., Harvey, T., Zaghari, B., Weddell, A., and White, N. (2017, January 13–16). Integrated Smart Bearings for next Generation Aero-Engines Part 1: Development of a Sensor Suite for Automatic Bearing Health Monitoring. Proceedings of the 1st World Congress on Condition Monitoring, ILEC Conference Centre, London, UK.
30. A Self-Powered Smart Roller-Bearing Based on a Triboelectric Nanogenerator for Measurement of Rotation Movement10.1002/admt.201800219 ↗
31. Zhang, Y., and Cao, J. (2018, January 9–12). Development of Self-Powered Smart Bearing for Health Condition Monitoring. Proceedings of the 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Auckland, New Zealand.10.1109/AIM.2018.8452340 ↗
32. High-Temperature Self-Powered Sensing System for a Smart Bearing in an Aircraft Jet Engine10.1109/TIM.2020.2971288 ↗
33. Cavaliere, P. (2019). Clean Ironmaking and Steelmaking Processes: Efficient Technologies for Greenhouse Emissions Abatement, Springer.10.1007/978-3-030-21209-4 ↗
34. Lenard, J.G. (2014). Primer on Flat Rolling, Elsevier. [2nd ed.].
35. Thermal Stresses in Work Rolls during the Rolling of Metal Strip10.1016/S0924-0136(98)00449-X ↗
36. Finite Elements Prediction of Thermal Stresses in Work Roll of Hot Rolling Mills10.1016/j.proeng.2010.03.076 ↗
37. Chiesa, S. (2008). Affidabilità, Sicurezza e Manutenzione nel Progetto dei Sistemi, CLUT.
38. The Sendzimir Cold Strip Mill10.1007/BF03377833 ↗
39. Brusa, E., and Lemma, L. (2006, January 4–7). A Multi-level Approach for the Mechanical Design of Cluster Mills for Cold Rolling of Thin Steel Products. Proceedings of the 8th Biennal ASME Conference on Engineering Systems Design and Analysis, ESDA, Turin, Italy.10.1115/ESDA2006-95075 ↗
40. Research on Vertical Vibration of Hot Rolling Mill under Screw-down-Strip Combined Excitation10.21595/jve.2019.20933 ↗
41. Brusa, E., Calà, A., and Ferretto, D. (2018). Systems Engineering and Its Application to Industrial Product Development, Springer International Publishing. Studies in Systems, Decision and Control.10.1007/978-3-319-71837-8 ↗
42. Thermal condition monitoring of large smart bearing through fiber optics sensors10.1080/15376494.2019.1655611 ↗
43. Ng, T.-H., and Liao, W.-H. (2004, January 14–18). Feasibility Study of a Self-Powered Piezoelectric Sensor. Proceedings of the Smart Structures and Materials 2004: Smart Electronics, MEMS, BioMEMS, and Nanotechnology, International Society for Optics and Photonics, San Diego, CA, USA.10.1117/12.539706 ↗
44. Spencer, S., and Emmons, S.A. (2009). Backing Assembly for Use in Z-Mill Type Rolling Mills. (WO2009041946A1).
45. A core reference ontology for steelmaking process knowledge modelling and information management10.1016/j.compind.2021.103574 ↗
46. Vibration analysis of Sendzimir cold rolling mill and bearing fault detection10.1243/09544062JMES1540 ↗
47. A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills10.1016/j.jprocont.2015.08.010 ↗
48. Kumar, S., and Madhab, G.B. (2006, January 9–13). Development of a DSP-Based Motor Health Monitoring System. Proceedings of the 2006 IEEE International Symposium on Industrial Electronics, Montreal, QC, Canada.10.1109/ISIE.2006.295943 ↗
49. De Pasquale, G., Brusa, E., and Soma, A. (2009, January 1–3). Capacitive Vibration Energy Harvesting with Resonance Tuning. Proceedings of the 2009 Symposium on Design, Test, Integration Packaging of MEMS/MOEMS, Rome, Italy.
50. Electromechanical Tuning of Self-Sensing Piezoelectric Transducers10.1177/1045389X9800900306 ↗
51. Kashyap, R. (2009). Fiber Bragg Gratings, Academic Press.10.1016/B978-0-12-372579-0.00007-7 ↗
52. Yin, S., Ruffin, P.B., and Yu, F.T.S. (2017). Fiber Optic Sensors, CRC Press. [2nd ed.].10.1201/9781420053661 ↗
53. Kreuzer, M. (2009). Strain Measurement with Fiber Bragg Grating Sensors, HBM. HBM Darmstadt S2338-1.0.
54. Ramakrishnan, M., Rajan, G., Semenova, Y., and Farrell, G. (2016). Overview of Fiber Optic Sensor Technologies for Strain/Temperature Sensing Applications in Composite Materials. Sensors, 16.10.3390/s16010099 ↗
55. Yao, K., Lin, Q., Jiang, Z., Zhao, N., Tian, B., Shi, P., and Peng, G.-D. (2018). Modeling and Analysis of a Combined Stress-Vibration Fiber Bragg Grating Sensor. Sensors, 18.10.3390/s18030743 ↗
56. Wang, W., Yu, Y., Geng, Y., and Li, X. (2015, January 10). Measurements of Thermo-Optic Coefficient of Standard Single Mode Fiber in Large Temperature Range. Proceedings of the 2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, Beijing, China.10.1117/12.2193091 ↗
57. Roman, M., Balogun, D., Zhuang, Y., Gerald, R.E., Bartlett, L., O’Malley, R.J., and Huang, J. (2020). A Spatially Distributed Fiber-Optic Temperature Sensor for Applications in the Steel Industry. Sensors, 20.10.3390/s20143900 ↗
58. Discrimination between Strain and Temperature Effects Using Dual-Wavelength Fibre Grating Sensors10.1049/el:19940746 ↗
59. Wavelength Division Multiplexing: Ultra High Speed Fiber Optics10.1109/4236.670678 ↗
60. (2021, May 03). Available online: https://www.phase-trans.msm.cam.ac.uk/2007/tetra/A/A-Pages/Image0.html.
61. (2021, May 03). Available online: https://www.koyo.eu/it/media-downloads/catalogo-jtekt-on-line.html.
62. Bearing Fault Detection and Fault Size Estimation Using Fiber-Optic Sensors10.1016/j.ymssp.2018.10.035 ↗
63. (2021, May 03). Available online: http://www.crsrl.com/steel-mill-and-sendzimir-steel-mill.html.
64. Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills10.1016/j.ins.2013.06.045 ↗
65. Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain10.1016/j.ymssp.2015.07.022 ↗
66. Fault detection and isolation of bearings in a drive reducer of a hot steel rolling mill10.1016/j.conengprac.2015.02.001 ↗
67. Agliullin, T., Gubaidullin, R., Sakhabutdinov, A., Morozov, O., Kuznetsov, A., and Ivanov, V. (2020). Addressed Fiber Bragg Structures in Load-Sensing Wheel Hub Bearings. Sensors, 20.10.20944/preprints202009.0017.v1 ↗
68. Unified Equations for the Slope, Intercept, and Standard Errors of the Best Straight Line10.1119/1.1632486 ↗
69. Error Measures for Fuzzy Linear Regression: Monte Carlo Simulation Approach10.1016/j.asoc.2016.04.013 ↗
70. Model Based Design of complex aeronautical systems through Digital Twin and Thread concepts10.1109/JSYST.2019.2925627 ↗
71. Brusa, E., Ferretto, D., and Calà, A. (2015, January 28–30). Integration of Heterogeneous Functional-vs-Physical Simulation within the Industrial System Design Activity. Proceedings of the 2015 IEEE International Symposium on Systems Engineering (ISSE), Rome, Italy.10.1109/SysEng.2015.7302774 ↗