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      Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

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            Various data-driven methods have been applied to predict machine health indicators especially in the field of prognostics. Machine health indicators reveal the condition of equipment and/or its components including bearings by monitoring their operation data such as frequency vibration. To aid the prediction of the machine health indicators, this study applies the BDQRA method to monitor the health of bearings as a component of the machine. The BDQRA method involves applying data compression techniques like feature extraction to the bearing vibration data, to extract the most important features like time-domain, frequency domain, and time–frequency domain features. Due to the complexity of the feature extraction process, this study proposes fast Fourier transformation for the data compression. This is followed by obtaining a time series profile of the bearing vibration data to analyse the health status of component bearing. It the uses change-point analysis to predict the period at which the bearing health deterioration is imminent. Since the bearing health deterioration could be due to the independent operation of a component bearing or through communication between the component bearing and other components (or bearings) within the process machinery, the method also applies the principle of interaction effect to investigate the contributions from the other components of the machinery to the health deterioration of the component bearing detected. The accuracy of the prediction of the point of imminent health deterioration of the component bearing is investigated by comparing the outcome of the BDQRA method with the outcome of other methods published in literature which have been applied to the dataset used in this study. The findings reveal the BDQRA method have comparative advantages to the methods used in the related studies.


            Author and article information

            ScienceOpen Preprints
            30 July 2021
            [1 ] DEKRA Organisational & Process Safety, PHI House, Southampton Science Park, Southampton SO16 7NS
            [2 ] Centre for Geo-Information Studies School of Architecture, Computing and Engineering Room KD2.28, The Knowledge Dock, Docklands campus University of East London, London, E16 2RD
            Author notes
            Author information

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            : 30 July 2021

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Time series,Data mining statistics,Engineering,Regression analysis
            Data Mining,Change-point Detection ,Data-driven Methods , Regression Modelling ,Interaction Effect,Process Fault Detection,Rolling Bearing,Bearing Fault


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