Predictive Fault Modeling for Process Deviation Detection
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Solution Overview
Problem
Existing methods for predicting process deviations in process engineering plants, such as painting plants, are not reliable and efficient, lacking a systematic approach for early detection and analysis of fault causes.
Innovation Solution
A method for predicting process deviations using a predictive model based on machine learning, which includes automatic detection of fault situations, determination of fault causes, and prioritization of process values, utilizing a fault database and historical data for anomaly detection and fault analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error prediction methods are used, then the system can detect faults, but the prediction reliability and efficiency are insufficient
Solution Approach 1:
The system performs preliminary actions by storing historical fault data and training the neural network model in advance. The model is pre-trained with balanced data containing both normal and fault conditions, enabling early and reliable fault prediction before actual deviations occur, thus improving both reliability and efficiency.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based fault detection systems with an intelligent neural network model. This substitution enables the system to automatically learn patterns from historical data and make accurate predictions, significantly improving prediction reliability while maintaining high efficiency through automated processing.
2Measurement precision
If more process values are monitored to improve detection accuracy, then the measurement precision increases, but the system complexity increases
Solution Approach 1:
The neural network model serves as a universal processing unit that handles multiple process values simultaneously. Instead of requiring separate analysis methods for each parameter, the single model processes temperature, humidity, pressure, and other variables together, improving detection precision without proportionally increasing system complexity.
Solution Approach 2:
The patent merges multiple data processing functions into a unified neural network framework. Historical data storage, feature extraction, pattern recognition, and prediction functions are combined in one integrated system, allowing comprehensive monitoring of multiple process values while maintaining manageable system complexity through unified architecture.
Data Source
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AI summary
The invention relates to a method for predicting process deviations in a processing system, for example in a painting system, by means of which process deviations can be easily and reliably predicted, said method comprising the following: - automatically creating a prediction model; - predicting process deviations during operation of the processing system using the prediction model.