How Accenture Minimizes Downtime with Predictive Maintenance Models | Experiment Exchange скачать в хорошем качестве

How Accenture Minimizes Downtime with Predictive Maintenance Models | Experiment Exchange

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 Subscribe to Experiment Exchange on your favorite podcast platform, available now on Spotify, Apple Podcasts, and more! ✔️ Learn more about SigOpt: https://sigopt.com ✔️ Follow us on Twitter at   / sigopt   ✔️ Learn more about Accenture: https://www.accenture.com