Machine Learning Model Training With Multi-Interval Normal Data
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Solution Overview
Problem
Conventional methods for training machine learning models struggle to effectively detect abnormal system conditions, particularly in devices with intermittent operations, as they require selecting only one time period for training data and fail to combine datasets across multiple intervals or include predicted future values.
Innovation Solution
An information processing apparatus and method that acquires normal data from multiple intervals, combines it to generate combined data, and uses this data to train a machine learning model that outputs a score indicating the system's normal condition, allowing for the detection of abnormal conditions by incorporating both past and predicted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only one time period is selected for training data, then the training process is simple, but the detection accuracy of abnormal conditions is insufficient
Solution Approach 1:
The patent combines multiple time periods of normal operation data into a unified training dataset. The data combining unit integrates data from different time periods (e.g., morning operation, afternoon operation) to create a comprehensive training set, enabling the machine learning model to learn from diverse operational patterns and improve detection accuracy across various conditions.
Solution Approach 2:
The patent segments the operational data into multiple distinct time periods and processes each segment separately before combining them. The data acquiring unit divides the dataset into multiple time periods, allowing selective acquisition of normal data from each segment, and the combining unit then integrates these segmented datasets systematically.
2Quantity of substance
If data from multiple intervals are combined, then the training data quantity increases, but the data processing time increases
Solution Approach 1:
The patent performs preliminary processing of data from multiple time periods before combining them. The data acquiring unit pre-acquires and prepares normal data from each time period separately, organizing the data in advance so that the combining unit can efficiently integrate pre-processed datasets without incurring excessive processing delays during model training.
3Adaptability or versatility
If normal data from multiple time periods are used, then the model generalization improves, but the risk of including abnormal data increases
Solution Approach 1:
The patent extracts only the normal data portions from each time period while excluding abnormal data. The data acquiring unit is configured to acquire specifically normal data from multiple time periods, filtering out abnormal conditions. This extraction approach ensures that the combined training dataset contains only representative normal operational patterns, improving model generalization without compromising reliability.
Data Source
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AI summary
An information processing apparatus (10) acquires pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, combines the acquired pieces of normal data to generate combined data, and performs machine learning using the combined data to train a machine learning model (14c) that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of measurement data.