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AI-Driven Reliability Engineering at Enterprise Scale: A Blog Series

08.22.2022

 

Introduction

Step Changes in Reliability Engineering with the Technological Revolution 

Reliability engineering as a discipline traces its origins to the 19th-century industrial revolution. Industrial pioneers such as George Westinghouse and Thomas Edison embraced technology and illuminated the world with novel innovations. Progress in technology laid the foundation to transform the economy from manual handcrafts to mechanized manufacturing at unprecedented scale and value. As the inventions became critical to society, so did the need to maintain, monitor, and ensure safe, reliable industrial operations. To achieve these goals, early technologists began to focus on studying the behavior and failure of operating machines, paving the way for data collection and continuous analysis of machine health. 

Fast forward to the 21st century, the field of reliability engineering is thriving. Reliability engineers study and develop methods for enhancing the safety and reliability of complex technological systems, such as nuclear power plants, petrochemical plants, hazardous waste facilities, maritime systems, transportation networks, infrastructure, and manufacturing plants. Every time we turn on a light switch, fill our vehicles with fuel, or fly in an airplane, we take for granted the scientific principles and methods that allow these assets to operate safely and reliably. We’ve come to rely on these advancements in our everyday lives, taking for granted how revolutionary it was for these innovations to become commonplace.  

Turning Point in Industrial Reliability and Safety 

Today, we’re amid another technological revolution that is driving even faster change: the digital transformation revolution. Technologies such as industrial internet-of-things, elastic cloud computing, big data, and artificial intelligence digitize the entire enterprise value chain and enable a step change in engineering, mathematics, and empirical modeling.  

In this blog series, we will explore how the confluence of these technologies is disrupting the centuries-old discipline of reliability engineering to advance industrial reliability and safety: 

  1. Digital Twins – the Foundation of AI-Driven Reliability 
  2. The Case for AI-Driven Asset Reliability 
  3. C3 AI’s Approach to AI-Driven Asset Reliability 
  4. AI Insight to Action for Asset Reliability 

Up Next: Digital Twins – the Foundation of AI-Driven Reliability 

 


About the Authors 

Shrey Satpathy (author) is a Senior Data Science Instructor at C3 AI where he works with customers and customer-facing teams to implement cutting-edge machine learning applications on the C3 AI Platform. He holds a Bachelor's and Master's degree in Nuclear Engineering and has deep expertise in computational modeling of thermal hydraulic systems. He also holds a Master's degree in computer science focusing on machine learning and artificial intelligence. He is excited to be innovating at this intersection of Physics and Artificial Intelligence. 

Lisa Luh (editor) is a Senior Product Marketing Manager at C3 AI, working primarily on the C3 AI Reliability Suite. Prior to C3, Lisa worked in business development for IBM’s cloud business. Lisa has an MBA from the Wharton School of the University of Pennsylvania and a B.S. from the Haas School of Business, University of California, Berkeley. 

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