

Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi - #540
Nov 29, 2021
Shayan Mortazavi, data science manager at Accenture, dives into innovative predictive maintenance strategies tailored for heavy industries. He discusses a deep learning framework aimed at preventing equipment failures in oil and gas sectors. The conversation highlights the transition from traditional maintenance to advanced machine learning techniques, detailing the challenges of utilizing LSTMs for anomaly detection and the importance of human labeling in model building. Shayan emphasizes the integration of sensory data to optimize machine health monitoring and improve predictive accuracy.
AI Snips
Chapters
Transcript
Episode notes
Shayan's Career Path
- Shayan Mortazavi's background is in mechanical engineering in the energy sector.
- He transitioned to data science after working with simulations and material science, realizing data's potential.
Industrial Analytics at Accenture
- Accenture's Industrial Analytics Group focuses on applying machine learning in heavy industries and resources.
- They address problems like production efficiency, supply chain, and predictive maintenance.
Predictive Maintenance Overview
- Predictive maintenance seeks to maximize asset reliability and availability.
- Traditional methods include fixed-based and time-based maintenance, while newer methods leverage sensor data.