Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

Mohamed Elforjani, Suliman Shanbr

    Research output: Contribution to journalArticlepeer-review

    85 Citations (Scopus)
    105 Downloads (Pure)


    Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.

    Original languageEnglish
    Pages (from-to)5864-5871
    Number of pages8
    JournalIEEE Transactions on Industrial Electronics
    Issue number7
    Early online date27 Oct 2017
    Publication statusPublished - 1 Jul 2018


    • Acoustic emission (AE)
    • Gaussian process regression (GPR)
    • artificial neural network (ANN)
    • condition monitoring
    • remaining useful life (RUL)
    • slow speed bearings
    • support vector machine regression (SVMR)


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