Register Here To View: Predictive Maintenance - Fantasy or Reality?

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WEBINAR DESCRIPTION:

Advances in AI and machine learning have been tremendous in recent years and impact our lives in many different and significant ways. We’ve seen self-driving cars become a reality, we are able to communicate with our homes using Alexa, and we are able to draft responses to our emails using AI virtual assistants.

Naturally, we expect similar advancements within heavy industries. Predictive Maintenance has been hailed as the Holy Grail for industries including oil and gas and maritime, where equipment failures are costly and safety is a priority. The reality is disappointing, and that is that we have fallen short.

We’re constantly told that the technology is there – look at AlphaGo, DeepMind, or Libratus consistently outperforming humans at more complex tasks. But we are talking about building models to describe the behavior of equipment under stochastic processes – like wind generation or oil production – with tremendously complex underlying physical behavior. These are not easily encapsulated into a set of fixed rules and would rely on many examples and boundaries to be successful.

Statistically (and luckily!), equipment fails very rarely, leaving us with a difficult predicament – how can we successfully build a model to identify specific failure modes on equipment, let alone predict it?

In this webinar, we will discuss unique industrial challenges and how you can build robust data sets for applying machine learning models

Learning Takeaways:

1. Why analytics in industrial space is more challenging than analytics in consumer space
2. What we can learn and leverage from successful analytics projects
3. Methods and tools for getting started with AI in industrial space

ABOUT THE PRESENTER

Alexandra is the CEO and co-founder of Unifai, a software company focused on building the tools needed for engineers and data scientists to develop high-quality training data for heavy asset industries. Prior to founding Unifai, Alexandra worked as a mechanical engineer with a focus on computational methods for the nuclear, military, and oil & gas industries before transitioning to a career as a data scientist at Arundo Analytics and later as Chief Data Scientist at Atea, a Scandinavian IT provider.