The Industrial Internet: The Future Is Healthy

This is the third post of a five-part weekly series exploring the Industrial Internet in the New Industrial Age. If you missed the beginning of the series, catch up by reading The Journey to the Industrial Internet and The Industrial Internet: The Future Is Big.

So why bother? Why gather and store massive amounts of data? It makes it harder to manage, harder to store, harder to use and harder to derive meaning and value. Why? The reason lies in the insights we derive from this data—the history that tells us today’s anomaly is tomorrow’s catastrophe. The history that helps us define a better future.

Data science is the study of data. It brings together math, statistics, data engineering, machine learning, analytics and pattern matching to help us derive insights from data. Today, industrial data is used to help us determine the health of our assets and to understand if they are running optimally or if they are in an early stage of decay. We use analytics to predict future problems and we train machine learning algorithms to help us identify complex anomalies in large data sets that no human could interpret or understand on their own.

The rationale behind using data science to interpret equipment health is so we can avoid unplanned downtime. Reducing down time increases uptime, and increased uptime leads to increases in production, power, flight and transportation. It ensures higher return on assets, allowing companies to derive more value from investment, lowering total cost of ownership and maximizing longevity.

Of course, equipment is used in processes, and like equipment health, we also look at and monitor process health so we can drive toward process optimization. Ensuring the maximum power is generated, or that the perfect dryness is achieved in processed goods, allows us to maximize value in power sold or in goods produced. Process health and process optimization then leads to increased yield and throughput, which drives more profit.

So we gather data and store data to learn what proper equipment health is and to maintain the equipment at optimal levels, driving return on assets and profit. The more data we have, the better our understanding, the better our prediction, the better our longevity and the tighter our tolerances.

Note: This post is part three of a five-week series examining the journey of the Industrial Internet.

Brian Courtney

A recent transplant to the MidWest, Brian thinks Big Data “rocks.” He’s recently taken the Analytics piece of GE’s business under his wing, so if you have thoughts on any of these – MidWest, Big Data or Predictive Analytics – even rocks – follow Brian on Twitter @brianscourtney.

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