Machine Learning Analytics

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Machine learning is all the rage, with business leaders scrambling to understand how it can benefit their organizations, and for some, even what machine learning is.  One thing is clear: the onslaught of data from the internet of things has made quickly scaling machine learning and advanced analytics the key to optimizing enterprise decision-making, operations, and logistics.

An enterprise-grade machine learning solution begins with three core capabilities:

  1. predictions without relying on knowledge of past events
  2. analysis and visualization of time series data
  3. optimized decision-making under uncertain conditions.

With these, an enterprise can put its data to work to improve operations and planning.

advanced-machine-learning

Handy resources to learn more about machine learning:

State of Enterprise Machine Learning

Major Roadblocks on the Path to Machine Learning

Mainstreaming Machine Learning

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National Grid Webinar: Answering Your Questions

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Recently David Salisbury, Head of Network Engineering for National Grid and Neil Barry, Senior Director EMEA at Space-Time Insight, presented the webinar “How Analytics Helps National Grid Make Better Decisions to Manage an Aging Network“, hosted by Engerati.  [Listen to the recording here.] Unfortunately, not all the submitted questions were able to be answered in the time allotted.  However, responses have been provided in this post.

How were pdf data sources incorporated into your analytics? How will that be kept up to date?

To correct to the discussion in the webinar, pdf data sources were not analysed in the valves and pipeline use cases. For the corrosion use case, data from pdf reports was manually rekeyed into the analytics solution.

 

Are there mechanisms built into the system that facilitate data verification and data quality monitoring?

In the general case, metrics were computed for data completeness (e.g., of the desired data, how much was actually available) and confidence (e.g., how recent was the data we used). For the corrosion use case, there are checks for data consistency and completeness.  For pipelines and valves, these metrics have not yet been fully configured.

 

Could you describe how this helps with the audit trail?  As the system changes, the current snapshot is updated.  How do you show the status at a certain point in the past when a decision was made?

For the corrosion use case, the history is stored and accessible, providing an audit trail. The foundation analytics does offer a ‘time slider’ that delivers animated time series data, making it easy for the user to go back in time.  However, this is not currently configured for National Grid.

 

Please provide specific examples of how decisions were made based on analytics and demonstration of analytics/predictive analysis

David described an example at around the eight minute mark into the webinar – budgets used to be set locally, but the insight from analytics might show that a particular type of problem is located in a specific geographic area. This can help with decisions around investment and risk.

 

How have you defined Asset Health? What data is required to assess?

Models for asset health were agreed upon by National Grid and Space-Time Insight during the implementation process. For pipelines, as was mentioned in the webinar, two of the data sets are Close Interval Potential Survey (CIPS) and Inline Inspection (ILI). For valves, a number of data sets are used, including test results and work orders.

 

Did you look at techniques to predict issues based on historical data…so you can target risk areas?

This has not been implemented by National Grid.  However, the product software has the capability to predict the probability of failure and the criticality of that failure, as one example.

 

Has Space Time insight worked on developing a situational intelligence tool for electric distribution and/or transmission applications? Similar to the gas transmission monitoring developed for National Grid?

Yes, Space-Time Insight offers an asset intelligence solution for electricity transmission and distribution utilities.  More information is available online.

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