Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

About This Episode

Today we’re joined by Shayan Mortazavi, a data science manager at Accenture.

In our conversation with Shayan, we discuss his talk from the recent SigOpt HPC & AI Summit, titled A Novel Framework Predictive Maintenance Using Dl and Reliability Engineering. In the talk, Shayan proposes a novel deep learning-based approach for prognosis prediction of oil and gas plant equipment in an effort to prevent critical damage or failure. We explore the evolution of reliability engineering, the decision to use a residual-based approach rather than traditional anomaly detection to determine when an anomaly was happening, the challenges of using LSTMs when building these models, the amount of human labeling required to build the models, and much more!

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Today’s show is brought to you by our good friends at SigOpt. Building effective models is a scientific process that requires experimentation to get right. With SigOpt, modelers design novel experiments, explore modeling problems and optimize models to meet multiple objective metrics in their iterative workflow. Whether tracking your training runs or running at scale hyperparameter optimization jobs, SigOpt is designed to meet your needs. Learn why teams from PayPal, Two Sigma, OpenAI, Numenta, Accenture and many more rely on SigOpt by signing up to use SigOpt for free forever at sigopt.com/signup.

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