Abstract
In this work, we make use of the Model-of-Signal technique to perform lubrication monitoring of a large industrial worm gear motor. We assume sensor measurements to be modelled by autoregressive processes and exploit the edge-computing capabilities of programmable logic controllers to perform the Recursive Least Squares algorithm to identify them. Then, we use those models to compute indicators able to diagnose the lubricant level within the gearbox and compare them to statistical indexes, which are traditionally used for monitoring. The aim of this application is to show how to build a condition monitoring infrastructure in an industrial environment able to detect possible occurring faults locally and acquire knowledge about them by exchanging information with external computers, paving the way towards Intelligent Maintenance Systems in Industry 4.0.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gouriveau, R., Medjaher, K., Zerhouni, N.: From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics. Wiley, Hoboken (2016)
Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006)
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., Siegel, D.: Prognostics and health management design for rotary machinery systems - reviews, methodology and applications. Mech. Syst. Signal Process. 42(1–2), 314–334 (2014)
Isermann, R.: Model-based fault-detection and diagnosis-status and applications. Annu. Rev. Control 29(1), 71–85 (2005)
Cerrada, M., Sánchez, R.V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J.V., Vásquez, R.E.: A review on data-driven fault severity assessment in rolling bearings. Mech. Syst. Signal Process. 99, 169–196 (2018)
Tulleken, H.J.: Grey-box modelling and identification using physical knowledge and Bayesian techniques. Automatica 29(2), 285–308 (1993)
Isermann, R.: Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer Science & Business Media, Berlin (2006)
Söderström, T., Stoica, P.: System Identification. Prentice Hall, Hoboken (1989)
Barbieri, M., Bosso, A., Conficoni, C., Diversi, R., Sartini, M., Tilli, A.: An onboard model-of-signals approach for condition monitoring in automatic machines. In: Enterprise Interoperability: Smart Services and Business Impact of Enterprise Interoperability, pp. 263–269. Wiley, Hoboken; ISTE, London (2018)
Barbieri, M., Diversi, R., Tilli, A.: Condition monitoring of ball bearings using estimated AR models as logistic regression features. In: 2019 18th European Control Conference (ECC), pp. 3904–3909. IEEE (2019)
Barbieri, M.: Seamless infrastructure for “Big-Data” collection and transportation and distributed elaboration oriented to predictive maintenance of automatic machines. Master’s thesis, University of Bologna 10 (2017)
Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Hoboken (1999)
Rauber, T.W., de Assis Boldt, F., Varejão, F.M.: Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(1), 637–646 (2014)
Wei, B., Gibson, J.: Comparison of distance measures in discrete spectral modeling. In: Proceedings of the 9th Digital Signal Processing Workshop (2000)
Acknowledgements
The authors would like to thank SITMA MACHINERY S.p.A.\(^*\) for supporting this project with the best-suited equipment and facilities and for providing insight and expertise that greatly assisted our work.
\(^*\) https://www.sitma.it/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Barbieri, M., Mambelli, F., Diversi, R., Tilli, A., Sartini, M. (2022). Condition Monitoring by Model-of-Signals: Application to Gearbox Lubrication. In: Zattoni, E., Simani, S., Conte, G. (eds) 15th European Workshop on Advanced Control and Diagnosis (ACD 2019). ACD 2019 2018. Lecture Notes in Control and Information Sciences - Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-85318-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-85318-1_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85317-4
Online ISBN: 978-3-030-85318-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)