Stitcher for Podcasts

Get the App Open App
Bummer! You're not a
Stitcher Premium subscriber yet.
Learn More
Start Free Trial
$4.99/Month after free trial

Episode Info

Episode Info: Role of Data in Reliability With the advent of Industry 4.0, data and connectivity have enabled equipment to become continuous factories of data. Whether process data, master data or realtime data, it has never been a more challenging time to adjust and harness the power of seemingly huge piles of potential information. Traditional maintenance and reliability are undergoing a tremendous shift as organizations become more data-driven. Sean Rosier and Nathanael Ince of PinnacleART are on the show to help us put in context, the relevance of data in reliability. In this episode, you’ll learn: Where to start implementing a data-driven program The influence of technologies like machine learning and digital twins Getting started on using data for reliability Arising challenges and possible solutions around data-driven strategies Where to start on a data-driven program Before diving into collecting data around your facility, you have to be fairly clear on the end goal. What state would you like the data to help you reach? Is it knowing your utility consumption vs production metrics? Is it equipment health? Aside from the expected outcomes supported by this data, it is also important to understand the frequency interval and the most crucial data to focus on.  Other considerations can be : Plan out a data collection strategy that is robust Have a master asset list  – Which can get assembled by doing a walk down in the plant, or using P&ID documents Have a way to visualize the assets in your portfolio – Which might help in understanding relationships between processes and equipment What is a digital twin? A digital twin is simply a virtual representation of a physical asset. It allows us to run simulations on the virtual copies, look at the outcomes and use the results to influence the existing asset. This concept has a direct impact on reliability in that the relevant personnel can run criticality analyses on the virtual models before implementing anything on the ground. Not only does it save time, but it also avoids most of the unforeseen implementation costs. What s more, reliability engineers can incorporate dynamic (real-world) influences to have an accurate picture of the outcome.  Where do we start with data-driven reliability? A good starting point would be having the end in mind. This sets the context for the resource requirement in implementing data-driven strategies.  Other steps to consider are not limited to: Ensuring you have the foundational data e.g. criticality analyses of assets Selecting the appropriate data that will help support your analysis of progress ML Big Data in Reliability Smaller companies are putting together innovation groups which proceed to deploy Machine Learning Projects as pilots. However, these pilots are run in isolation from the input of maintenance and reliability personnel. Another challenge is that even with successful pilots, organizations will still have a hard time integrating the Machine Lear...
Read more »

Discover more stories like this.

Like Stitcher On Facebook


Episode Options

Listen Whenever

Similar Episodes

Related Episodes