Sometimes, a single predictive analytics catch can save millions of dollars for a company.

Schneider Electric Software considers IIoT, which is shorthand for industrial IoT, as the foundation of digital transformation in the enterprise. The company has over 2 million software licenses deployed at more than 100,000 sites with about 20 billion connected data streams and 10 trillion data points archieved each day, according to Sean Gregerson, director of sales for Schneider Electric.

“We enable all of our IIoT solutions through what is called our EcoStruxure framework and that allows us to bridge the OT/IT divide through common device integration and security framework,” Gregerson said.

The result is that companies increase their profits while enabling energy efficient operations, using closed loop business operations through IIoT. There are several examples of how this has worked to save money for customers that work with Schneider.

SEE: How a Toyota supplier saved $1 million by updating its tech infrastructure (TechRepublic)

For instance, at American Electric Power (AEP), a collaborative work effort between AEP’s M&D Center resulted in a gas turbine blade being repaired in advance of a breakdown. The company used advanced predictive analytics for an early warning notification of the issue. If the problem was not detected, it would have continued to worsen possibly to the point where the turbine rotor would have needed replacement at a cost estimated at $19 million and a loss of power generation of over $1.2 million, Gregerson said.

“We’re trying to catch these problems at the very independent stages, well before we’re going to hit high or low operational set points. We’re typically monitoring these assets at a fidelity of once per minute and that gives us more than enough fidelity to identify problems that are in the early stages of degradation,” he said.

At Tata Power in India, there was a gas turbine early warning catch that saved the company nearly $300,000 after employees realized one of the bypass valves of a low pressure heater was partially open when it should have been completely closed. It caused condensate flow to bypass through the heater and resulted in a higher extraction temperature and inefficient operations.

At Duke Energy, there is a centralized monitoring center for monitoring their power generation plants. One of Duke’s steam turbines showed a slight increase in vibration after maintenance was performed, and the predictive asset analytics software triggered an early warning, alerting employees that the unit was in the early stages of blade separation. This early identification and action resulted in a savings of more than $4.1 million by preventing further damage to the equipment and extended loss of power generation.

Another example was at Southern Company, an Atlanta-based power company, where early warning detection showed that a motor coupling shim pack was coming loose. This could have caused a potentially catastrophic event because it would have caused loss of generation capacity and damage to the equipment. The savings were estimated at more than $250,000.

At EDF Energy in the UK, Schneider developed three monitoring teams: One for fossil generation, one for hydro generation, and one for nuclear generation. An early warning showed a bearing problem with a gas turbine that was in the early stages of mechanical failure. Maintenance was scheduled and the early warning and repair was estimated to have saved more than $1 million by eliminating further damage to the equipment and lost production.

How to implement IIoT

Many companies don’t know where to start with industrial IoT implementation.

At Schneider, Gregerson said that most companies have to first identify an internal business use case by looking at failures that have occurred, and identify where a predictive solution and early warning notification could have saved money. “Usually customers are able to identify that fairly easily because these industrial asset equipment problems result in single issues that cost hundreds of thousands to millions of dollars each,” he said.

Once the business use case is developed, the most important assets are identified, and a pilot program is started with software implemented for those assets. Eventually the predictive models are built out, results are interpreted, other industrial equipment is added to the program, and everything is monitored from a central location, he said.

“These cases really show the need to have a more predictive approach so that companies can find these problems before they become operational issues that need to be dealt with in an immediate way,” Gregerson said.

Three takeaways for TechRepublic readers

  1. Industrial IoT can help companies identify problems early through predictive analytics and save money as a result.
  2. At American Electric Power, the company saved more than $20 million through IIoT, and Tata Power in India had a gas turbine early warning catch that saved nearly $300,000.
  3. To implement IIoT, a company needs to first identify where they’ve had problems in the past, and which assets are most valuable.

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