Operation Data Segmentation for Clean Anomaly Detection Training
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Solution Overview
Problem
Existing machine training models struggle to accurately detect anomaly values from industrial system operation data, especially when trained on data that includes both normal and anomaly periods, leading to inefficiencies in maintaining industrial systems.
Innovation Solution
A training data generation apparatus that acquires operation data, divides it into temporary training and test data, detects anomaly values, and generates training data by excluding anomaly periods, ensuring the final data set includes only normal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If operation data including both normal and anomaly periods is used as training data, then the machine training model can be trained with sufficient data volume, but the anomaly detection accuracy deteriorates
Solution Approach 1:
The operation data is segmented into normal periods and anomaly periods through temporal division. The detection unit identifies anomaly periods by comparing operation data across different time windows, then the generation unit excludes these anomaly periods from training data. This segmentation allows sufficient data volume from normal periods while ensuring anomaly detection accuracy by removing contaminated anomaly segments.
2Measurement precision
If anomaly periods are excluded from training data to improve detection accuracy, then the anomaly detection accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary anomaly period detection and exclusion before the training data is actually needed. The detection unit pre-identifies anomaly periods by analyzing operation data patterns, and the generation unit pre-excludes these periods to create clean training data. This preliminary action ensures high anomaly detection accuracy while managing processing complexity by performing exclusions in advance rather than during real-time detection.
3Measurement precision
If manual inspection is used to identify anomaly periods, then the training data quality improves, but the time consumption increases
Solution Approach 1:
The system performs self-service anomaly period identification through automated detection algorithms. The detection unit automatically identifies anomaly periods by comparing operation data patterns across different time windows without requiring manual inspection. This self-service approach maintains high training data quality while dramatically reducing time consumption compared to manual methods.
Data Source
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AI summary
According to one embodiment, a training data generation apparatus (1) includes an acquisition unit (11), a division unit (12), a detection unit (13), and a generation unit (14). The acquisition unit (11) acquires operation data related to an operation state of a device in a predetermined period. The division unit (12) divides the operation data into at least temporary training data and temporary test data. The detection unit (13) detects an anomaly value from the temporary test data based on the temporary training data. The generation unit (14) generates training data from the operation data by excluding an anomaly period in which the anomaly value is detected from the predetermined period.