The times given in the conference program of TDWI München digital correspond to Central European Time (CET).
By clicking on "EVENT MERKEN" within the lecture descriptions you can arrange your own schedule. You can view your schedule at any time using the icon in the upper right corner.
Deep learning methods archive stunning performances in many disciplines like computer vision or natural language processing. For factory automation, deep learning is applied to detect anomalies and monitor the machine condition to increase productivity. In practice, we need to overcome multiple challenges: low quality datasets, missing domain knowledge and lack of performance metrics standards. In this session, we explain the usage of open source frameworks and configurable deep learning tools to create a condition-based maintenance system.
Target Audience: Decision Makers, Data Scientist, Factory Automation Experts
Prerequisites: Basic knowledge in data processing.
Level: Basic
Tom Hammerbacher is Dual Student Information Technology at Phoenix Contact. He is a Data Scientist for Anomaly Detection and Condition Monitoring, System Manager - Data Collection, Storage, and Evaluation.
Vortrag Teilen