BJEST: A reverse algorithm for the real-time predictive maintenance system

Dheeraj Bansal*, David J. Evans, Barrie Jones

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    An algorithm, to estimate the machine system parameters from the motion current signature, based upon non-linear time series techniques for use in the real-time predictive maintenance system is presented in this paper. Earlier work has introduced the use of a neural-network approach to learn non-linear mapping functions for condition monitoring systems. However, the performance of the neural-network largely depends upon the quality of the training data, and that of the quality and type of the pre-processing of the input data. A reverse algorithm called BJEST (Bansal-Jones Estimation), for estimating the machine input parameters using the motion current signature, has been designed and proven to be successful in estimating the macro-dynamics of the motion current signature. This motivated the enhancement of the predictive analysis to incorporate non-linear characteristic of the motion current signature. The results show considerable improvement in the estimation of the parameters using the enhanced BJEST algorithm due to estimation consistency, hence, improving the real-time predictive maintenance system.

    Original languageEnglish
    Pages (from-to)1068-1078
    Number of pages11
    JournalInternational Journal of Machine Tools and Manufacture
    Issue number10
    Publication statusPublished - 1 Aug 2006


    • Machine parameter
    • Motion current signature
    • Neural-network
    • Non-linear time series
    • Predictive maintenance
    • Surrogate data test


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