Sensor State Classification Using Mirrored Data Normalization
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Solution Overview
Problem
Existing machine learning models for plant control are highly dependent on the specific conditions and usage situations of sensors, making it difficult to apply models generated from one sensor's data to another sensor with different characteristics, leading to inefficiencies in model adaptation and increased time and costs for generating new models for each sensor.
Innovation Solution
An information processing system that includes a state data acquisition device, a first classification device for generating a determination line, a mirror image data generation device, and a second classification device to generate a determination model, which improves model versatility by canceling out obvious trends in sensor data and allowing for automatic adaptation across different sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is generated using measurement data from one sensor, then the model can accurately evaluate data from that specific sensor, but the model cannot be applied to other sensors with different usage situations or installation conditions
Solution Approach 1:
The patent extracts and removes sensor-specific characteristics (usage situation, installation conditions, deterioration state) from the measurement data through preprocessing operations. By separating these individual characteristics from the core evaluation targets, the model can focus on universal patterns while ignoring sensor-specific variations, enabling cross-sensor application without sacrificing accuracy.
Solution Approach 2:
The patent transforms measurement data by applying parameter changes that normalize sensor-specific variations. Through preprocessing steps such as normalization, standardization, or feature transformation, the data from different sensors with varying usage conditions are converted to a common parameter space, allowing a single model to accurately evaluate all sensors regardless of their individual characteristics.
2Measurement precision
If a new machine learning model is generated for each sensor to ensure accurate evaluation, then measurement precision is maintained, but time consumption and costs increase significantly
Solution Approach 1:
The patent creates a universal machine learning model that can evaluate multiple sensors simultaneously. By designing the model with multi-functionality to handle various sensor types and conditions through a single unified framework, the system eliminates the need to generate separate models for each sensor, thereby maintaining high evaluation accuracy while dramatically reducing the time and resources required for model generation.
Solution Approach 2:
The patent performs preliminary preprocessing of measurement data to remove sensor-specific characteristics before model training. By conducting this preprocessing action in advance, the model can be trained once on preprocessed data from multiple sensors, eliminating the need for repeated model generation for each sensor while ensuring accurate evaluation through the preliminary removal of confounding variables.
3Adaptability or versatility
If artificial modification is applied to the generated model to make it applicable to other sensors, then model versatility is improved, but it becomes difficult to modify the model when it is hard to objectively digitize differences between sensor usage situations
Solution Approach 1:
The patent replaces the manual, mechanical process of artificial model modification with an automated computational preprocessing system. By using algorithmic preprocessing operations to objectively transform and normalize sensor data, the system eliminates the need for difficult manual model adjustments, especially when sensor usage situation differences cannot be easily digitized. This substitution of automated processing for manual modification significantly reduces complexity while improving versatility.
Data Source
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AI summary
An information processing system includes a state data acquisition device configured to acquire state data indicating an operation state of a plant, a first classification device configured to generate a determination line used to classify the state data, a mirror image data generation device configured to generate mirror image data obtained by mirroring the acquired state data using the generated determination line, and a second classification device configured to generate a determination model on the basis of the state data and the generated mirror image data.