Fault Handling in Industry 4.0: Definition, Process and Applications

Area of Science:

• Industrial Engineering
• Machine Learning
• Data Science

Background:

• Modern industries require increased productivity and reduced production loss for economic competitiveness.
• Efficient fault management, including rapid fault amendment, is crucial for maintaining production lines.
• Data-driven methods, particularly machine learning, can support complex fault management in data-rich industrial environments.

Purpose of the Study:

• To review existing literature on fault management methods in industrial production.
• To provide an overview of requirements and methods for fault detection, classification, and prioritization.
• To identify research gaps, specifically in machine learning approaches for fault prioritization.

Main Methods:

• Literature review of data-driven methods for industrial fault management.
• Analysis of prerequisites for fault detection, classification, and prioritization.
• Examination of machine learning applications in fault handling.

Main Results:

• Fault detection and classification methods are relatively well-researched.
• Significant research gaps exist in the application of machine learning for fault prioritization.
• Current fault prioritization often relies on expert opinions due to limited data-driven methods.

Conclusions:

• There is a need for further research into machine learning methods for fault prioritization in industrial settings.
• Integrating expert knowledge with data-driven approaches is essential for effective fault prioritization.
• Advancing fault prioritization techniques can significantly enhance industrial fault management and competitiveness.