Attribute-Based Operation State Prediction Under Device Variability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing prediction systems for target devices, such as plants, fail to accurately forecast future operation states due to neglect of attribute information like device specifications and environmental factors, which significantly influence behavior.
Innovation Solution
A prediction system that includes a storage unit for operation state history and attribute information, a first acquisition unit for attribute information filters, a second acquisition unit for operation state filters, an extraction unit to extract relevant histories, and an estimating unit to predict future operation states based on filtered data, considering attribute information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attribute information (device specifications, environmental factors) is incorporated into the prediction system, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The prediction system is segmented into distinct functional modules: an acquisition unit that collects attribute information and operation data, an analysis unit that processes the data separately, and a prediction unit that generates forecasts. This segmentation allows the system to handle complex attribute information through modular processing, improving prediction accuracy while managing system complexity through organized structure.
Solution Approach 2:
The analysis unit serves as an intermediary between the acquisition unit and prediction unit. It processes raw attribute information and operation data, transforming them into meaningful features before passing them to the prediction unit. This intermediary layer simplifies the overall system architecture by handling the complexity of data processing in a dedicated component.
2Reliability
If comprehensive attribute information is collected and analyzed, then prediction reliability improves, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing attribute information in an organized manner before prediction is needed. The acquisition unit collects and structures device specification data and environmental factors in advance, creating a ready-to-use database that can be quickly accessed during prediction operations, thus reducing real-time processing time while maintaining comprehensive data analysis.
Data Source
AI summary
A prediction system includes a storage unit that stores a history of an operation state of each of a plurality of target devices and attribute information indicating an attribute of each of the plurality of target devices; a first acquisition unit that acquires an attribute information filter condition in which at least one attribute information included in attribute information of a prediction target device is specified; a second acquisition unit that acquires an operation state filter condition in which at least one operation state included in a history of an operation state of the prediction target device is specified; an extraction unit that extracts a history of an operation state of a target device satisfying the attribute information filter condition and the operation state filter condition; and an estimating unit that predicts the operation state of the prediction target device based on the extracted history of the operation state.