Building a Brain for Industrial America

This article was written in partnership with Bloomberg Media Studios.  It is an interview with Pat Byrne, Chief Executive Officer, GE Onshore Wind. Pat previously served as CEO, GE Digital.

Question:

Let’s explore how your products function as the brains for a variety of industries, starting with the power industry. How does upgrading the power grid help renewable sources of power, with lower carbon footprints, expand their markets?

Pat Byrne:

Our digital technologies work across power generation, transmission and distribution. In power generation, we improve the efficiency of the equipment. Take a wind turbine, as an example. The wind doesn’t blow all the time. So rather than service that turbine at regular intervals, you’ll want to perform maintenance after a certain number of rotations. Our predictive analytics, using historical data and sensors on the equipment, help you determine when that will be. And it allows you to schedule the maintenance, rather than have to perform it suddenly, when a part on the turbine breaks. 

In transmission, our software enables utilities to forecast demand and supply better. That way, they don’t oversupply the grid, creating more CO2 than is necessary. In distribution, our software helps utilities keep the power grid stable, despite surges in use, weather events, or other stresses on the system. 

Question:

When hurricanes, tornadoes or other natural disasters hit, power often is interrupted. How can digital tools fix what generally requires a worker in a truck to repair?

Pat Byrne:

Digital systems can help limit the outages. “Self-healing” systems can detect a downed power line and reroute that power to a feeder line. Customers near where the line went down will be without power until a crew arrives, but most customers on the line will have their power restored quickly and automatically. 

During 2020’s Hurricane Sally, Alabama Power implemented 39 self-healing operations and 22,000 customers were restored in under two minutes with no human intervention.

Question:

The COVID-19 pandemic has resulted in a new hybrid model of work at home and work in the office. While many teams are able to work from home, many workers, such as those in utilities and manufacturing, often need to work on location. How are they able to work remotely, when their roles normally require a physical presence?

Pat Byrne:

With the onset of the pandemic, white collar workers were often able to work from home. But so were some blue-collar workers in water utilities and manufacturing companies. How were they able to work remotely, when their roles normally require a physical presence?

A great example is in Massachusetts where the City of Haverhill’s water treatment plants have used our automation software for many years for remote operations and monitoring. The automation software captures precise, real-time data from sensors and control equipment, displaying it on graphical screens that guide operators through the right actions.

In normal times, the Haverhill may have 10 or more people at a given plant. When COVID-19 hit, the team was prepared to keep water flowing to 58,000 homes and businesses while working from safe locations. They were able to operate with folks monitoring the plants using laptops or tablets from home. Remote teams see the same data and screens as if they were on the plant floor, monitoring everything from water levels and the revolutions per minute of pumps to the amount of treatment chemicals being dispersed. 

At its Integrated Smart Operations Center, New York Power Authority (NPYA) also uses online remote monitoring of power plants, sub-stations and power lines to increase plant efficiency and productivity, reduce unplanned downtime, lower maintenance costs and minimize operational risks. At the onset of the pandemic, and in less than 24 hours, they enabled 2,400 NYPA and New York State Canal team members to work safely from their homes. NYPA's suite of asset performance management software enabled critical infrastructure to remain operational while minimizing risk to the health of its workforce, with no significant operational events during this period.

Question:

What are some examples of manufacturing companies that have found insights lurking in their oceans of data?

Technologies such as Advanced Analytics are helping to transform and contextualize time-series and transactional data into actionable insights and uncover improvement potential. For example, data-driven predictive maintenance can save up to 12% of scheduled repairs, reduce overall maintenance costs up to 30% and breakdowns up to 70%. 

Ninety percent of Procter & Gamble’s production lines leverage Proficy Plant Applications to improve equipment specifications, quality/maintenance inspections, and production management. They collect and analyze data to eliminate stops, reduce line downtime and show where there is waste in the process. 

Another customer, GE Aviation, is deploying their next generation Manufacturing Execution System (MES) that will reduce the number of applications across 50+ manufacturing shops, maximizing efficiency of the shop floor while tracking the engine build genealogy to the final bolt put on an engine. GE Aviation’s OneMES roadmap leverages Proficy Plant Applications and its ability to handle mixed manufacturing environments – for one solution with MES, quality, and machine connectivity. With the MES system, GE Aviation can analyze an end-to-end manufacturing dataset, helping improve operations and maintain regulatory compliance. 

Question:

How does creating a “digital twin” of an industrial machine enable it to be more efficient?

A digital twin is a software representation of a physical thing or system that runs in tandem with its real-world counterpart. It has historical data about how that thing or system operates, and is updated with performance data from sensors tracking the physical thing or system. Combining the historical and performance data, it predicts when the thing or system will need to be repaired, or when it will fail. 

For instance, a gas turbine costs millions to build and repair – and if it stops working, it can cost millions in unplanned downtime. A digital twin helps optimize maintenance schedules and predict and avoid unplanned downtime. In the past 15+ years, we’ve saved our customers more than $1.6 billion in reduced operations and maintenance costs and avoided downtime.

Likewise, for grid operators, a digital representation of the power grid can produce cost reductions of up to 30%, reduce planning time for events by up to 20%, and reduce internal process costs by up to 7%. For manufacturers, digital twins can help identify the most optimal process to manufacture a product, helping plant operators ensure they are consistently delivering against quality, cost and volume objectives.