Key Takeaways
- Industrial artificial intelligence (AI) and machine learning (ML) are transforming the heavy-asset industry by providing actionable insights from large volumes of operational and maintenance data.
- ML models are being applied to support asset reliability, process stability, energy efficiency, and safety, with notable success in vibration monitoring and bearing failure prediction.
- AI and ML require special organizational and technical considerations, including high-quality data and adaptability to changing conditions.
Introduction to Industrial AI/ML
Industrial artificial intelligence (AI) and machine learning (ML) are revolutionizing the heavy-asset industry by enabling the analysis of vast amounts of data from sensors, distributed control systems, and other sources. To effectively incorporate AI/ML into industrial engineering, it is essential to understand the core concepts, including ML types and algorithms/models.
Industrial Engineering Insights
Industrial engineering has traditionally relied on deterministic models and human expertise to design, operate, and maintain complex production systems. However, the increasing scale, complexity, and data intensity of modern industrial facilities are outpacing these approaches. AI and ML offer mechanisms to digest and transform large volumes of operational and maintenance data into actionable insights.
Comparison of AI/ML Implementation
| Implementation | Description | Benefits |
|---|---|---|
| Deterministic Models | Traditional approach using first-principles analysis and human expertise | Limited scalability, prone to errors |
| AI/ML Models | Data-driven approach using machine learning algorithms and models | Improved accuracy, scalability, and adaptability |
| Hybrid Approach | Combining deterministic models with AI/ML models | Enhanced decision-making, improved asset reliability |
Case Study: Vibration Monitoring and Bearing Failure Prediction
A notable example of AI/ML implementation is vibration monitoring and bearing failure prediction on paper machine rolls. By applying ML models to a seven-year vibration database with multiple recorded failures, the system was able to predict bearing failures more accurately than human analysts. This demonstrates the potential of AI/ML to augment engineering judgment and improve asset reliability.
Bottom Line
The incorporation of artificial intelligence and machine learning into the heavy-asset industry is transforming the way industrial engineering is approached. By providing actionable insights from large volumes of data, AI/ML models can support asset reliability, process stability, energy efficiency, and safety. As the industry continues to evolve, it is essential to understand the core concepts, benefits, and challenges of AI/ML implementation to unlock its full potential and drive business growth. With proper implementation, AI and ML can augment engineering judgment, advising users through agentic AI advisors and enabling earlier fault detection, improved root-cause analysis, and optimized operational strategies.