Machine Learning Device Correlates Shipment Inspection and Alarm Data
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Solution Overview
Problem
The correlation between shipment inspection information and operation alarm information for objects, such as motors, has not been adequately addressed, making it difficult to analyze and understand the actual use conditions, leading to inefficiencies in inspection and maintenance.
Innovation Solution
A machine learning device that observes shipment inspection information and operation alarm information to generate a learning model through clustering, enabling the identification of inspection items influencing operational alarms and predicting fault occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to examine the correlation between shipment inspection information and operation alarm information, then the analysis can be performed with simple tools, but enormous efforts and time are required to organize and comprehend the data
Solution Approach 1:
The patent replaces manual mechanical analysis methods with machine learning algorithms that automatically process and analyze the correlation between shipment inspection information and operation alarm information. The learning unit uses clustering algorithms to group similar data patterns, eliminating the need for manual data organization while achieving comprehensive correlation analysis.
Solution Approach 2:
The patent introduces a machine learning device as an intermediary system between the raw inspection/alarm data and the correlation analysis results. This intermediary automatically performs data preprocessing, clustering, and pattern recognition, bridging the gap between unorganized data and meaningful insights without requiring manual intervention.
2Reliability
If detailed inspection of all shipment inspection information is performed to identify correlations with operation alarms, then the inspection quality improves, but the device complexity and analysis burden increase significantly
Solution Approach 1:
The patent extracts only the most relevant features and patterns from the comprehensive shipment inspection information using machine learning clustering. Instead of analyzing all raw data in detail, the system identifies and extracts key correlation patterns between inspection parameters and subsequent alarms, maintaining high reliability while reducing complexity.
Solution Approach 2:
The patent transforms the raw shipment inspection information and operation alarm data into clustered groups based on similarity. By changing the parameter representation from individual data points to clustered patterns, the system simplifies the analysis while preserving the essential correlations between inspection results and operational issues.
3Measurement precision
If comprehensive clustering analysis is performed on all shipment inspection information and operation alarm information, then the correlation identification accuracy improves, but the computational processing time increases
Solution Approach 1:
The patent segments the large dataset of shipment inspection information and operation alarm information into smaller, manageable clusters based on similarity. The learning unit performs clustering analysis to divide the data into groups that share common characteristics, enabling efficient processing while maintaining accurate correlation identification within each cluster.
Solution Approach 2:
The patent performs clustering analysis on representative samples and key features rather than exhaustively processing every single data point in detail. By focusing computational resources on the most informative portions of the data through clustering, the system achieves accurate correlation identification with reduced processing time.
Data Source
AI summary
A machine learning device which learns a correlation between shipment inspection information obtained by inspecting an object in shipment thereof and operation alarm information issued during operation of the object, includes a state observation unit which observes the shipment inspection information and the operation alarm information; and a learning unit which generates a learning model based on the shipment inspection information and the operation alarm information observed by the state observation unit.