As companies look to make Industry 4.0 applications like AI/ML, digital twins, and operational orchestration a central part of their operations, they are again confronting challenges with silos.

Increasingly, data usage is the differentiator — the factor critical to the productivity, reliability, and sustainability of heavy assets.

As wind turbines age, how can wind operators get more out of their fleets?

In 2021, we can expect to see more owner-operators and OEMs turn to third parties for digital solutions.

When industrial leaders consider AI-driven predictive maintenance solutions, most choose one of two paths:

1. Generalized platforms that enable do-it-yourself (DIY) Machine Learning model-building — like AWS SageMaker or Azure Machine Learning Studio.

2. Dedicated, industry-specific applications with pre-built data science models that offer AI-enabled insights.

Many wind operators are still grappling with the imprecision of traditional power curve-based methods.

Power performance now holds great opportunity for wind operators.

Like shale production, data science is challenged by extracting, refining, and controlling the input that makes it productive.

"…Even when the apparatus exists, novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong.”

All analytics aren’t created equal. Here's a simple guide to wind analytics, from the basic end of the maintenance spectrum to the most advanced.