Jan 29 2018

Energy Firms Can Gain Insights Faster with Edge Computing and IoT

By collecting and analyzing data at the network edge, Schneider Electric can predict mechanical problems in oil pumps more quickly.

Oil and natural gas fields aren't exactly known as hospitable locations with plentiful connectivity.

That's why global industrial giant Schneider Electric SE has turned to edge computing and Internet of Things gateways to collect and analyze data from connected oil pumps. Since the data is processed at the location of connected devices  at the network edge  it does not need to be sent back to a central data center.

By embracing edge computing, Schneider and other companies that work in the energy and utilities industries can more quickly discover mechanical or maintenance issues. This is especially helpful in locations where wireless connectivity is not strong or abundant.

The global market for edge computing is expected to grow to $6.7 billion by 2022, up from about $1.5 billion in 2017, according to an October report from research firm MarketsandMarkets. According to data cited by The Wall Street Journal, Gartner reports that by 2021, 40 percent of enterprises will have an edge computing strategy in place, up from about 1 percent in 2017.

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The Benefits of Edge Computing

Computing at the network edge, as Network World reports, allows data produced by IoT devices "to be processed closer to where it is created instead of sending it across long routes to data centers or clouds." Doing this, "lets organizations analyze important data in near real-time."

Don Duet, president and COO of Vapor IO, a startup working with mobile infrastructure providers to build and deploy edge servers at cell towers, tells the Journal that it usually takes 150 to 200 milliseconds for data to travel from where it's generated to a cloud provider and back.

By placing servers or gateways closer to connected devices in the field, energy firms and other organizations can shorten that time to 2 to 5 milliseconds, significantly boosting the performance of applications or shortening the time it takes to get a reading from a piece of equipment.

The Journal reports:

In many edge computing scenarios, a piece of hardware called a gateway is located physically near the device. The gateway aggregates information from sensors, analyzes it with software, and pushes insights and data to a corporate cloud, when necessary. In other scenarios, servers and software form an "edge cloud" near the device itself.

Schneider Embraces Edge Computing for Predictive Maintenance 

Schneider Electric started tinkering with Microsoft's Azure IoT Edge last summer, which, the Journal notes, connects devices in the field to gateway hardware and is an extension of its Azure public cloud.

Cyril Perducat, executive vice president of digital services and Internet of Things at Schneider Electric, tells the Journal that the company is using the Microsoft solution to predict costly mechanical problems with rod pumps, which extract oil in remote locations without much wireless connectivity.

As a result, Schneider is selling a predictive maintenance solution as a new service offering to oil field customers. "We're able to go from describing a problem to recommending a path to prevent the problem even before it happens," Perducat tells the Journal.

It is important that Schneider helps oil and gas companies predict problems in equipment such as rod pumps days or weeks ahead of time, since any downtime will cause the companies to lose revenue.

Before Schneider began using edge computing, Perducat tells the Journal that "a remote terminal unit was responsible for gathering and analyzing data from the pump in real-time and sending a signal to an operator when an abnormality was detected."

Workers would then have to physically go to the rod pump to copy data such as temperature, pressure and sediment onto a hard drive and bring it back for analysis to discover any problems.

With the new Azure IoT Edge solution, gateways located near the pumps can aggregate data, analyze it in real time and predict pumps that might fail in the coming days or weeks. "We've moved from reactive to proactive," Perducat says.

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