Application of a real-time predictive maintenance system to a production machine system

Dheeraj Bansal*, David J. Evans, Barrie Jones

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    This paper develops earlier work that established the possibility of the classification of machine system parameters on the basis of motion current signature, using a neural network approach. A neural network requires a large amount of training data, which is impractical to generate using a production machine for real-time predictive maintenance system. Hence, a simulation model, which mapped the system parameters to the motion current signature, was developed. The accuracy of the system, to predict the changes in the value of the machine system parameter, is a direct function of the validity of the simulated data. Thus, the objective validation of the simulation model is important to ascertain that the simulation model is accurate with regards to its purpose. In this paper, the simulation model is validated against an on-line production machine. Various approaches to validate the simulation model are applied and a simulation model is developed.

    Original languageEnglish
    Pages (from-to)1210-1221
    Number of pages12
    JournalInternational Journal of Machine Tools and Manufacture
    Issue number10
    Publication statusPublished - 1 Aug 2005


    • BJEST
    • Graphical comparison
    • Motion current signature
    • Neural network
    • Sensitivity analysis
    • Simulation model
    • TuneLearn
    • Validation
    • Visual comparison


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