Will control or reliability propel IoT in utilities?


532029221Whether it’s a smart water meter or a complex combustion turbine, expectations of reliability for complex, connected machines have increased across the board.

Due to higher visibility and the availability of advanced analytics, companies know they should be able to identify emerging safety and reliability issues in these assets. By doing so, they could avoid significant litigation costs related to environmental impacts of a failed asset. And they could avoid costly repairs that negatively affect customer satisfaction ratings.

Today, many of these devices have embedded sensors that, when analyzed, give us a picture of the health of the device. And, if the device is connected to the internet, then it is now available to The Internet of Things.

Finding answers in noisy data

However, the growing amount of data generated from sensors on monitoring equipment creates an overwhelming amount of noise. This abundance of data can mask important insights and make it difficult for companies to confidently detect emerging trends embedded in their critical asset data – much less figure out how to make timely improvements.

By applying analytics to these massive data streams, organizations can understand what is most likely to happen next and evaluate the tradeoffs that will be required to optimize quality and safety. With the right technology, business leaders can make more consistent, better-informed decisions, and respond faster to emerging issues and events.

“Sensors are the DNA roadmap to allow unrelated things to talk through embedded microprocessors. The result will be a distributed intelligence platform with progressively more intelligence on premise, at the substation and in the central hub.”[1]

- Jason Handley, Director of Smart Grid Technology and Operations, Duke Energy

The ability to remotely control an asset via an internet-connected device has caused alarm for some in the security arena. Rest assured that assets are not running on auto-pilot. Process controls still place critical decisions with real people, not machines. Regarding cyber security, greater visibility into asset performance offered by IP connected devices gives even earlier warning into cyber attacks.

For asset-intensive industries, early detection of events, situations or changes in asset conditions can have direct bottom line impact on operational costs. Consider these benefits:

  • Detect problem trends as they happen, and identify root causes proactively.
  • Operate more efficiently by filtering out unrelated information and focusing on insights that matter.
  • Respond to real-time production conditions and quality of manufactured feed stocks.
  • Predict impending events by identifying conditions in real-time that pose risks to reliability and safety.

Business users now demand the same insight for asset and process optimization that was once the domain of engineers, just at the right business level. This deeper situational awareness is quickly emerging as a competitive differentiator for companies in many industries. But not without analytics. Check out this Introduction to IoT for more information.

[1] enterpriseappstoday.com/business-intelligence/how-iot-will-change-big-data-analytics.html.


About Author

Alyssa Farrell

Product Marketing Manager, Energy and Sustainability

Alyssa Farrell leads global industry marketing for SAS’ business within the energy sector, including Utilities, Oil and Gas. In this role, she focuses on the SAS solutions that help optimize our energy infrastructure by applying predictive analytics to complex data. She currently serves on the Advisory Committee of the Research Triangle Cleantech Council and co-leads the Program and Communications Action Committee, as well as a Working Group of the Utility Analytics Institute. She is a member of the Society of Petroleum Engineers (SPE). Farrell regularly speaks with trade associations, analysts, and the press about the opportunities organizations have to effectively manage a sustainable energy analytics strategy and drive healthy economic growth. Prior to joining SAS, Farrell was a senior consultant in the Deloitte Public Sector practice. In this capacity, she was a project manager for state-wide and county-wide systems implementations and was responsible for user acceptance testing, change management and training, and middleware technology selection. She is a graduate of the Eller College of Management at the University of Arizona, where she earned her MBA degree with a concentration in Management Information Systems. She also holds a Bachelor of Arts degree from Duke University.

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