Bearing Fault Model for an Independent Cart Conveyor

  • Marco CocconcelliEmail author
  • Jacopo Cavalaglio Camargo Molano
  • Riccardo Rubini
  • Luca Capelli
  • Davide Borghi
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)


Independent cart conveyor system is an emerging technology in industries, trying to replace servo motors and kinematic chains in several applications. It consists of several carts on a closed-loop path, each of which can freely move with respect to the other carts. Basically, each cart is an servo linear motor, where the windings and the drives are on the frame and the magnets are on the moving carts together with a feedback device (e.g. a Hall sensor to track the position). The drive controls and actuates each cart independently according to the motion profile loaded. From a mechanical point of view, the carts are connected to the frame through a series of rollers placed on and under a mechanical guide. The rollers may be subject to a premature wear and the condition monitoring of these components is a no trivial challenge, due to non-stationary working conditions of variable speed profile and variable loads. This paper provides a bearing fault model taking into account the motion profile of the cart, the mechanical design of the cart, the geometry of the conveyor path, the expected loads and the type of fault on the roller bearings.


Independent cart system Ball bearings Fault model Linear motors 



The authors are grateful for the National University Research Fund (FAR 2016) of the University of Modena and Reggio Emilia - Departmental and Interdisciplinary Projects (DR. 73/2017, Prot. n. 37510-27/02/2017) and the support from Tetra Pak Packaging Solutions.


  1. 1.
    Rockwell Automation: iTRAK The Intelligent Track System Increase machine flexibility and throughput to enhance overall productivity.
  2. 2.
    Beckhoff Automation: XTS. The eXtended Transport System.
  3. 3.
  4. 4.
    Molano JCC, Rossi S, Cocconcelli M, Rubini R (2017) Dynamic model of an independent carts system. In: Advances in Italian mechanism science. Mechanisms and machine science, vol 47, pp 379–387Google Scholar
  5. 5.
    Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126CrossRefGoogle Scholar
  6. 6.
    Poyhonen S, Jover P, Hyotyniemi H (2004) Signal processing of vibrations for condition monitoring of an induction motor. In: Proceedings of 1st international symposium control, communications signal processing, pp 499–502Google Scholar
  7. 7.
    Nandi S, Toliyat H, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans Energy Convers 20(4):719–729CrossRefGoogle Scholar
  8. 8.
    Randall RB (2011) Vibration-based condition monitoring: industrial, aerospace and automotive application. Wiley, HobokenCrossRefGoogle Scholar
  9. 9.
    Curcurú G, Cocconcelli M, Immovilli F, Rubini R (2001) On the detection of distributed roughness on ball bearings via stator current energy: experimental results. Diagnostyka 51(3):17–21Google Scholar
  10. 10.
    Immovilli F, Cocconcelli M, Bellini A, Rubini R (2009) Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Trans Ind Electron 56(11):4710–4717CrossRefGoogle Scholar
  11. 11.
    Hayes M (1996) Statistical Digital Signal Processing and Modeling. Wiley, HobokenGoogle Scholar
  12. 12.
    Trajin B, Chabert M, Regnier J, Faucher J (2009) Hilbert versus Concordia transform for three-phase machine stator current time- frequency monitoring. Mech Syst Signal Process 23(8):2648–2657CrossRefGoogle Scholar
  13. 13.
    Salami M, Gani A, Pervez T (2001) Machine condition monitoring and fault diagnosis using spectral analysis techniques. In: Proceedings of the 1st international conference on mechatronics, pp 690–700Google Scholar
  14. 14.
    Wang W, McFadden PD (1993) Early detection of gear failure by vibration analysis I. Calculation of the time-frequency distribution. Mech. Syst. Signal Process. 7(3):193–203CrossRefGoogle Scholar
  15. 15.
    Wang W, McFadden PD (1996) Application of wavelets to gearbox vibration signals for fault detection. J Sound Vib 192(5):927–939CrossRefGoogle Scholar
  16. 16.
    Cerrada M, Snchez R-V, Li C, Pacheco F, Cabrera D, Valente de Oliveira J, Vsquez RE (2018) A review on data-driven fault severity assessment in rolling bearings. Mech Syst Signal Process 99:169–196CrossRefGoogle Scholar
  17. 17.
    McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high frequency resonance technique a review. Tribol Int 117:3–10CrossRefGoogle Scholar
  18. 18.
    McFadden PD, Smith JD (1984) Model for the vibration produced by a single point defect. J Sound Vib 96:69–82CrossRefGoogle Scholar
  19. 19.
    McFadden PD, Smith JD (1984) The vibration produced by multiple point defects in a rolling element bearing. J Sound Vib 98:263–273CrossRefGoogle Scholar
  20. 20.
    Su YT, Lin SJ (1992) On initial detection of a tapered roller bearing frequency domain analysis. J Sound Vib 155:75–84CrossRefGoogle Scholar
  21. 21.
    Ho D, Randall RB (2000) Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech Syst Signal Process 14:763–788CrossRefGoogle Scholar
  22. 22.
    D’Elia G, Cocconcelli M, Mucchi E (2018) An algorithm for the simulation of faulted bearings in non-stationary conditions. Meccanica 53(45):1147–1166MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marco Cocconcelli
    • 1
    Email author
  • Jacopo Cavalaglio Camargo Molano
    • 1
  • Riccardo Rubini
    • 1
  • Luca Capelli
    • 2
  • Davide Borghi
    • 2
  1. 1.Department of Sciences and Methods of EngineeringUniversity of Modena and Reggio EmiliaReggio EmiliaItaly
  2. 2.Tetra Pak Packaging SolutionsModenaItaly

Personalised recommendations