AI Failure Cause Analysis
Quickly pinpoint root causes from complex sensor data
The ‘Failure Root Cause Analysis Solution’ uses AI to analyze sensor data from manufacturing processes, swiftly identifying anomalies and failure causes. It reduces reliance on skilled experts and streamlines maintenance for greater efficiency.
Use Cases
Discover how this solution can be applied in various scenarios.
Identify the Cause of Production Equipment Failures
Extraction of Possible Causes of Product Abnormalities
Understand the Relationships Between Equipment and Sensors
Solution Features
Learn about the key features that make this solution effective.
Options Include Libraries and Basic Applications
Combine Approaches for Comprehensive Understanding and Analysis
Choose Approach and Model Based on Data Characteristics
Using various models such as Graphical Lasso, which represents the complex interactions between sensors by connecting closely related variables and representing them as a "correlation graph," we can trace the cause of sensors that show abnormalities.
As shown in the image, by visualizing the degree to which the data from each sensor number is highly correlated, it is possible to identify the factors that influenced the failure and analyze the relationships between equipment that had not been recognized before.
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