Looking to enhance your maintenance operations and prevent unexpected failures? In this insightful webinar, Jules Oudmans from UReason walks you through how machine learning models can recognize asset and process degradations early, keeping your equipment running smoothly and efficiently. Discover how to deploy these models to streaming data for real-time insights and smarter decision-making.

Key Topics Covered:

⚙️ Machine Learning in Maintenance: Understand the role of machine learning models in optimizing maintenance strategies across industries.
🔄 Deploying Predictive Models: Learn how to integrate predictive maintenance models to identify failures before they happen.
🛠️ Model Deployment & Management: Explore the main algorithms and tools used in machine learning for maintenance, and see how to deploy and manage these models effectively.

Chapters:
00:00 Waiting Room
03:26 Introduction: Applying AI in Maintenance & Operations
04:57 About UReason
07:32 Definition of Artificial Intelligence
08:14 History of AI
11:21 Moore's Law
12:17 What it Brought Us
14:01 Compute Power Allows us to do More
14:45 Tools Have Become Easier & Affordable/Free
15:52 Machine Learning?
17:42 Supervised vs Unsupervised
18:20 What ML Methods to Apply?
21:03 Creating/Deploying ML Models
24:59 An Example of a ML Model - Soft Sensor
26:24 An Example of Soft Sensors
29:35 Demo of APM Studio and KNIME
48:32 Q&A

About the Speaker:

Jules Oudmans, co-founder of UReason, has extensive experience in operational intelligence and real-time analytics, working with global industry leaders such as Shell, BP, and Siemens. Jules specializes in applying AI to operational processes, helping businesses improve their asset performance and maintenance strategies.

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https://www.ureason.com/