Maintaining oil and gas machinery is expensive—but predictive maintenance models can help engineers minimize repairs and downtime.

Shayan Mortazavi and Alex Lowden, Data Scientists at Accenture in the Industrial Analytics Group, work on the development of predictive maintenance models to minimize downtime of systems. In this episode, they discuss the complications when building these models, such as limited access to failure data and the massive number of features available, as well as the need for explainability and interpretability in their models. They also share how SigOpt’s parallelism feature allowed them to accelerate model development.

• 1:27 - Intros
• 3:05 - Machinery maintenance, then vs now
• 4:06 - Goals of maintenance
• 6:49 - Challenges of predictive maintenance for oil and gas
• 8:31 - Human in the loop element
• 10:07 - Interpretability
• 11:42 - Using SigOpt to optimize hyperparameters
• 13:50 - Managing multiple LSTMs
• 16:38 - Using SigOpt's multimetric optimization
• 18:36 - Predicting ultimate machine failure
• 20:39 - Getting teams on board with AI-based tools
• 23:21 - Overconfidence of AI

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Learn more about Accenture: https://www.accenture.com

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