Softsensor System Integrating Multi-Source Sensor Data for Process Variable Prediction
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Solution Overview
Problem
Existing sensor network systems do not effectively collect and store measurement values from online, inline, and offline sensors, nor predict modeling results using new process variables in manufacturing processes such as biopharmaceutical, smart factory, food, and steel manufacturing.
Innovation Solution
A softsensor analysis and measurement system that collects and stores data from various sensors using a machine learning/deep learning module, applying new process variables to output results and feed them back to control computers in manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a sensor network system collects and stores sensor data in a server, then data collection and storage capability is improved, but the ability to predict modeling results using new process variables in manufacturing processes is not provided
Solution Approach 1:
The patent combines sensor data collection functionality with machine learning prediction functionality into a unified softsensor system. The server collects sensor data from multiple sources (online sensors, inline sensors, offline sensors) and integrates this data with process variables in a machine learning model to enable prediction of unmeasured process parameters, thereby merging data collection with predictive capability.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between collected sensor data and process variable prediction. The model processes collected data from various sensors and transforms it into predictive outputs for new process variables, serving as a mediator that enables prediction capability without requiring direct measurement of all process parameters.
2Adaptability or versatility
If the system collects and stores data from multiple sensor types (online, inline, offline), then comprehensive data collection is improved, but the complexity of the system increases
Solution Approach 1:
The patent designs a universal softsensor server that can collect, store, and process data from multiple types of sensors (online temperature/humidity/pressure sensors, inline IoT device sensors, and offline analysis device sensors) through a unified interface and machine learning framework, enabling multi-sensor integration without requiring separate processing systems for each sensor type.
Data Source
AI summary
Provided is a softsensor analysis and measurement system to provide the output by new progress variables based on collected data by a sort of sensors. The system comprises: an online sensor, an inline sensor, or an offline sensor; a machine learning/deep learning module; and a soft sensor server for collecting and storing online sensor data collected by the on-line sensor through a sensor network and a gateway, Ethernet, or WLAN (Wi-Fi), collecting and storing inline sensor data measured by an IoT device equipped with the inline sensor or offline sensor data measured by the offline sensor through an analysis device (dedicated analyzer), and outputting a result by applying new process variables (input) of a manufacturing process by using the machine learning/deep learning module, and feeding back and applying the new process variables of the manufacturing process to a control computer of the manufacturing process.