Model-based predictive maintenance techniques applied to automotive industry



Published Jun 30, 2018
Nicola Longo Valeria Serpi Giovanni Jacazio Massimo Sorli


In automotive industry, and generally in the mass production world, maintenance is a very critical issue requiring special attention since every single stop causes a huge loss in term of item produced due to very small cycle time.

Basing on this observations, in the last years, a lot of efforts has been put in failure prevention and condition based maintenance; as an example in Fiat Chrysler Automobiles (FCA) the WCM (World Class Manufacturing) became part of its culture and the area dedicated to Professional Maintenance makes possible many step forwards. The ways WCM reaches the zero breakdown are Time Based Maintenance (TBM) and Condition Based Maintenance (CBM) but further improvements can be reached  with focus on cost reduction and by optimizing the component usage without arriving to a fault.

In this paper, after an overview of maintenance techniques adopted in FCA plants worldwide, a model-based approach is suggested for a COMAU hemming tool named RHEvo. After the development of a simplified model, we try to estimate the actual status of internal components making use of Neural Network.

Focusing on the internal springs, the aging affects the elastic coefficient because of fatigue phenomena. As will be shown, under certain assumptions the cracks presence affects the nominal elastic coefficient; therefore, starting from the estimation coming from the Neural Network, it is possible to model an equivalent crack length. Finally, basing on stochastic crack growth model proposed by Yang and Manning an estimation of internal spring’s Remaining Useful Life Estimation (RULE) is calculated.

How to Cite

Longo, N., Serpi, V., Jacazio, G., & Sorli, M. (2018). Model-based predictive maintenance techniques applied to automotive industry. PHM Society European Conference, 4(1).
Abstract 863 | PDF Downloads 1366



automotive; predictive; spring

Technical Papers