Dynamic Batch Processing Interface for Real-Time Anomaly Modeling
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Solution Overview
Problem
Traditional industrial batch processing systems are inefficient in detecting anomalies, leading to delayed solutions and increased pressure to enhance batch processing efficiency as the number of products manufactured using batch processing methods continues to rise.
Innovation Solution
The implementation of an AI modeling system that integrates information technology (IT) and operational technology (OT) data to analyze collected data, identify baseline trends, and predict anomalous behavior in industrial batch processing systems, using a data model management architecture, pre-processing files, and automatic AI engines for real-time monitoring and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional testing procedures are used to analyze batch data, then measurement precision is maintained, but analysis time increases significantly (days or weeks)
Solution Approach 1:
The patent replaces traditional mechanical testing procedures with an AI-based automated analysis system. The AI engine processes batch data using machine learning algorithms, substituting manual or conventional mechanical testing methods with intelligent computational analysis, thereby reducing analysis time from days/weeks to much faster processing while maintaining anomaly detection accuracy
Solution Approach 2:
The patent introduces an AI engine as an intermediary between raw batch data and anomaly detection results. This AI intermediary automatically processes and analyzes the data, eliminating the need for lengthy traditional testing procedures while preserving measurement precision through sophisticated algorithmic analysis
2Measurement precision
If AI models are made more complex to improve anomaly detection capability, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements dynamic model selection and configuration capabilities that allow the system to adapt model complexity based on specific batch processing requirements. The user interface enables dynamic adjustment of model parameters and selection of appropriate AI algorithms, allowing the system to optimize between detection accuracy and computational complexity for different scenarios
Solution Approach 2:
The patent allows dynamic modification of model parameters through the user interface without requiring complex model restructuring. Users can adjust parameters such as sensitivity thresholds, data sampling rates, and model configuration settings, enabling the system to achieve improved anomaly detection precision through parameter optimization rather than increasing fundamental model complexity
3Measurement precision
If more data is collected and processed to improve model accuracy, then measurement precision improves, but energy consumption and processing time increase
Solution Approach 1:
The patent extracts and utilizes only the most relevant features and data points from the batch processing data through automated feature selection algorithms. The AI engine identifies and processes only the critical parameters that contribute to anomaly detection, eliminating redundant data processing and reducing computational energy consumption while maintaining model accuracy
Solution Approach 2:
The patent implements selective data processing that focuses computational resources on the most critical portions of the dataset. The system processes data at varying levels of detail based on batch characteristics, using full processing power only when anomalies are detected or suspected, thereby reducing overall energy consumption while maintaining detection accuracy
Data Source
AI summary
A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving data associated with one or more industrial devices of an industrial system, and retrieving one or more pre-processing files and one or more training datasets files associated with a model from a database, wherein the one or more pre-processing files are configured to transform the data, and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time. The instructions cause the processing circuitry to perform operations including receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display, and generating the model based on the training dataset files and the one or more inputs.