Ever since Thomas Edison launched the Pearl Street Station in New York City in 1882, electricity has played an integral role in the growth of commerce, and is now essential in everyday life. If you ever experienced an extended blackout in a city you know exactly what I mean.

For many years, power has been generated using steam turbines that are driven by either coal or gas-fired power plants. Typical operating procedures for these plants involve running the turbines continually for several years before taking them down for maintenance. Therefore, the design required that a machine that ran continually, and only started and stopped a few times in its lifetime.

With the advent of solar and wind power, these traditional power plants found themselves needing to start and stop more often. This is referred to as cycles within the plant, where—for example—the gas-fired power plant may only come on at night when the sun goes down, and the solar power plant is no longer able to generate electricity.

These cycles will cool down and contract and then heat up and expand the turbine impeller assembly, which makes it more susceptible to cracking. Also, the failure mode of these types of assets that are cycling up and down is very different from the behavior of a plant that is runs at a steady state for many months. Traditional predictability goes out the door.

In addition, other aspects come into play, such as different power utility companies and personnel, weather conditions, utility customer demands, and a host of other differences.

Producers of steam turbines are facing a whole new set of challenges versus what they were facing before with the greater adoption of alternative energy. They needed more information to be able to make better decisions about how to do maintenance on the equipment. Also, they needed better models that could be used to make design enhancements and improve the operating life of the equipment they are manufacturing.

The answer is to create a digital twin of the turbine made up of little digital twins of all the components that make up the unit to allow the monitoring and simulation of these devices in the field.

In addition to this, they should telemetry and sensors on the turbines that collect all that information and ingest it into a database which allows analytics to be performed on the data set.

The analytics will make it possible to improve optimization and "what if" scenarios such as:

  • "What if we increased the let down cycle and/or decreased the cycle, what impact does that have on the impeller's material characteristics?"
  • “What if I increase the turbine pressure ratio? Does it improve the performance of the combined cycle cogeneration system?”
  • "What if we scheduling of maintenance this month versus next month; does it increase the failure conditions of this specific turbine?”

With these insights, the team could make more informed decisions, which included:

  • Better information on how to stock parts across the supply chain, depending on operating conditions and projected failure rates
  • The ability to source parts based upon the projected performance of parts in the fleet. This led to better pricing negotiations with suppliers.
  • Less downtime for inspections as the wear can be predicted more accurately.
  • Better able to model the need for personnel and equipment deployments to specific facilities across a geographical area; better marring maintenance needs with available resources.
  • The ability to create customized maintenance schedules which included lubrication, cooling, optimization, and inspection requirements based upon the varying operating conditions
  • More effective scheduling of replacement unit manufacturing and MRO operations on plants based on field service projections. This helps reduce inventory and conserves cash.

These are the kinds of things that are possible when you start your journey to become a digital industrial. Come learn from our experiences at GE.

Miss the beginning of this series?  Read: 12 Manufacturing Tips for a Brilliant 2017. Tip 1: Manufacturing Physics

Ready for the last blog post of the series?  Read: 12 Manufacturing Tips for a Brilliant 2017. Tip 12- The Leadership Component of MES

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GE Digital

Driving Digital Transformation

GE Digital connects streams of machine data to powerful analytics and people, providing industrial companies with valuable insights to manage assets and operations more efficiently. World-class talent and software capabilities help drive digital industrial transformation for big gains in productivity, availability and longevity. We do this by leveraging Predix, our cloud-based operating system, purpose built for the unique needs of industry.

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