AI Failure Cause Analysis
Quickly Uncover Relationships in Complex Sensor Data
The "Failure Cause Analysis Solution" swiftly identifies potential causes of product anomalies and equipment failures by analyzing sensor data acquired during manufacturing. Traditional methods demand extensive checks and experienced personnel, while this AI-driven solution enhances equipment maintenance efficiency and enables maintenance without relying solely on expertise.
ソリューションの特徴
Main Usage Scenarios
Identify the Cause of Production Equipment Failures
Extraction of Possible Causes
of Product Abnormalities
Understand the Relationships Between Equipment and Sensors
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.
ソリューションの特徴
Solution Features
Options Include Libraries and Basic Applications
Combine Approaches for Comprehensive Understanding and Analysis
Choose Approach and Model Based on Data Characteristics
