August 16, 2018, 6:09 am

Big Data, Predictive Analytics and Reliability – Moving Beyond Better Maintenance

In earlier articles in this series, we have discussed the various functions within any business that can contribute to sound equipment reliability (design, operations, maintenance and supply).  We have also looked at the key tools and techniques that can be used by these functions, and where these may be applicable. 

In this article, we will continue our exploration of reliability topics by considering how developments such as the Industrial Internet of Things (IIOT), big data and analytics are contributing to reliability improvement.

We have previously explored the impact of these development on maintenance in our article ‘Big Data, Predictive Analytics and Maintenance’.  We have also explored the impact of these developments on traditional thinking about predictive maintenance in our article ‘The PF Interval – Is it Relevant in the world of Big Data?’

In this article, we will extend this exploration into the world of reliability engineering. Specifically, we will consider how the abundance of accessible and consistent data, combined with applications to support visualisation, modelling, analysis and machine learning are supporting the four key pillars of modern reliability engineering, namely:

  • Reliability by Design,
  • Operate for Reliability,
  • Maintenance Tactics Optimisation, and
  • Defect Elimination

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