Mean Time Between Failure for Predictive Maintenance Using Hadoop and PowerBI

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Wiranto Herry Utomo, et. al.


The use of Mean Time Between Failure in Predictive Maintenance is increasing and is in line with the number of industries that are turning to industry 4.0 . Previously, predictive maintenance is being done by analyzing each machine individually and manually calculate them. This caused the predictive maintenance to become somewhat complex and a long process while it should not be. Big data helps to organize the data needed to calculate mean time between f ailure ef ficiently and PowerBI helps to visualize and analyze said data. We use data f rom several machines which record their runtime, downtime, and the type of downtime to get the mean time between f ailure. Contrary to the majority of existing implementations that mostly use complex data to schedule predictive maintenan ce, Our f indings f ind that simple data is suf f icient as long as it is processed in an organized environment such as using big data and visualized clearly and well using visualization applications like PowerBI.


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How to Cite
et. al., . W. H. U. . (2021). Mean Time Between Failure for Predictive Maintenance Using Hadoop and PowerBI. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 4121–4131.
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