Is Big Data a big deal?

September 3 2013 - Drew Robb

The last decade has seen monumental advances in computer power. The average laptop, these days, would put a supercomputer of the nineties to shame. This has propelled the field of data analytics into the stratosphere. Calculations which used to take months can be accomplished in minutes.

Now factor in gains in sensor and wireless technology, which make it possible to gather an ocean of information from plants and turbomachinery. This combination of compute power, analytics and the assembly of huge quantities of data is known under the collective title of ‘Big Data.’

Turbomachinery product and service providers, such as GE, Siemens, Praxair, Woodward and Invensys, are already investing heavily in Big Data, to help users extend operating times of turbines and plants; better predict machine failures; and plan overhauls and maintenance cycles. Others caution, however, that it may be too much, too soon.

Klaus Brun, Senior Machinery Manager at the Southwest Research Institute (SwRI), for example, would rather use this computing power to compare massive quantities of measured data or for computational fluid analysis and finite element analysis programs rather than to determine performance optimization or maintenance cycles for operating gas turbines. In any case, many are convinced that investing now is well worth the risk and are pushing to be ahead of the curve. Praxair, an industrial gases company, for example, has established a central data monitoring center for all its facilities in the world, using Big Data technologies.

“With Big Data employed correctly, we can see if a facility in Argentina has problems and mitigate issues before they happen happen,” said Victor Rioli, Reliability Director in Global Operations Excellence at Praxair. “This means we can reduce our costs, our customers’ costs and minimize environmental impacts as there is no need to have an engineer stop in a facility once a month, just to see what’s going on.”

To increase efficiency and output

Similarly, GE Oil & Gas is implementing Big Data to increase the efficiency, output of its turbines used for onsite generation, as well as applications such as compression of natural gas for pipeline transmission. Additionally, Big Data can be used to improve the economics of extracting and transporting unconventional resources.

“A small increase of only one percent in several areas such as efficiency, reduced emissions, uptime, safety and productivity can mean millions of dollars in profitability, or make it viable to exploit resources in remote regions and harsh environments,” said Dan Heintzelman, President & CEO GE Oil & Gas. “If you can analyze geological and seismic data more rapidly and more deeply, you don’t waste time drilling at unproductive sites, for example. That’s why we are investing heavily in measurement and diagnostics technology in addition to lifecycle management.”

Analysis and implementation

Meanwhile companies such as Siemens, Woodward, Invensys, as well as IT partners such as Amazon.com are figuring how best to harness these vast stores of data to change the way turbomachinery is designed, operated and maintained. Instead of evaluating the gathered data to obtain a historical view of machine performance, it can be reviewed and acted upon in real time.

“Analyzing Big Data at rest is too slow,” said Greg Schulz, an analyst with IT consulting firm StorageIO. “You have to be able to process it as it comes in.” For example, the sensors on a jet engine generate several terabytes (TBs) of data every flight. Paul Maritz, CEO of IT firm Pivotal, explained that most analytics applications would first ingest the data, store it and then analyze it. His firm is figuring out the best way to use this information during the journey in order to optimize jet fuel consumption, improve engine efficiency and in the longer term, to drastically reduce maintenance costs.

Pivotal is owned mostly by IT giants EMC and VMware, but GE has rolled in a 10 percent stake which amounts to $110 million. GE is interested in the design of the next generation of enterprise software applications, those that will drive what it calls the Industrial Internet or the Internet of Things.

“As advanced telemetry is added into all devices, it will ramp up the data volume by two orders of magnitude, thus there is a need for real time analysis,” said Maritz. “A gas turbine, jet engine or locomotive can harness telemetry to deliver new value. By reviewing real time data, there is no need to break down an engine every 2,000 hours. This will enable OEMs to profitably change their business model to contracting for hours of operation, not sales of machines.”

Consequently, turbine OEMs and control system manufacturers are gearing up for what promises to be a revolution in plant control, profitability and efficiency. But far from being pie in the sky, they have already begun to add these capabilities into their turbines and compressors, and offer service contracts that take remote monitoring of turbomachinery to a new level.

(Read the complete article in the September-October issue of Turbomachinery International)