Product Performance Prediction Using Device Outlier Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In industrial production, the complexity of production lines and device operating environments leads to device faults, causing discrepancies between predicted and actual product performance, which traditional simulation methods fail to accurately address.
Innovation Solution
A product performance prediction modeling method that acquires device outlier data, production line configuration parameters, and product information to establish a machine learning model for simulation testing, enabling accurate prediction of product performance through machine learning training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation software is used for performance verification, then the prediction process is simple and fast, but the prediction accuracy deteriorates due to unknown differences between real manufacturing environment and analog data
Solution Approach 1:
The patent combines traditional simulation models with machine learning models to create a hybrid prediction system. The simulation model provides baseline performance data while the machine learning model learns from actual device outlier data, merging the advantages of both approaches to improve prediction accuracy while managing complexity through modular integration
Solution Approach 2:
The patent introduces device outlier data as an intermediary element that bridges the gap between simulation environment and real manufacturing environment. This intermediary data captures actual anomalies and uses them to train the machine learning model, enabling the system to adapt to real-world variations without requiring complete re-simulation
2Measurement precision
If more simulation parameters and data are used to improve prediction accuracy, then the prediction quality improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model using historical device outlier data and simulation data during the modeling phase. This preliminary training allows the model to learn complex patterns in advance, so that during actual prediction, the system can quickly process new data without requiring extensive real-time computation
Solution Approach 2:
The patent implements a dynamic prediction approach where the system adapts its processing based on the type and severity of device outliers. For common or minor outliers, the system uses faster prediction pathways, while for rare or critical outliers, it activates more comprehensive analysis modes, dynamically adjusting computational resources to balance accuracy and speed
Data Source
AI summary
Provided are a product performance prediction modeling method and apparatus, a product performance prediction method, a product performance prediction system, a computer device, and a storage medium. The product performance prediction modeling method includes: acquiring first sample data, the first sample data including device outlier data generated in a process of manufacturing a product by a device; acquiring a production line configuration simulation parameter of a production line relating to a location of the device, and product information of the product manufactured by the production line; selecting a simulation model to perform simulation test on the performance of the product, to obtain product performance simulation data; and inputting the device outlier data, the production line configuration simulation parameter, the product information and the product performance simulation data into a machine learning model to perform machine learning training, to obtain a product performance prediction model.