Modular Analytics Engine for Real-Time Industrial Control Modeling
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Solution Overview
Problem
Industrial monitoring and control systems face challenges in achieving real-time optimization due to the need for complex and specific models, which often result in simplified models that compromise performance and require significant manual input and adaptation, limiting their flexibility and adaptability across different applications and environments.
Innovation Solution
A modular analytics engine system that instantiates modules for modeling, optimization, classification, and control on a data-driven basis, allowing for agnostic and adaptive operations using annotated data structures, enabling flexible deployment and interaction between modules, and reducing the need for prior knowledge of specific systems or processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model predictive control (MPC) system simplifies the process model for computational efficiency, then real-time optimization speed is improved, but model quality and controller performance deteriorate
Solution Approach 1:
The patent segments the modeling and control process into distinct modular components: data collection modules, model generation modules, validation modules, and control modules. This allows complex models to be broken down into manageable segments that can be processed efficiently in real-time while maintaining overall model quality through systematic validation at each stage.
Solution Approach 2:
The patent performs preliminary model generation and validation offline before real-time control execution. Models are developed, validated, and stored in advance, allowing the real-time system to use pre-validated models without computational overhead, thus maintaining both speed and accuracy during actual control operations.
2Measurement precision
If custom models are developed specifically for each machine, then model accuracy for that machine is improved, but system complexity and manual adaptation requirements increase
Solution Approach 1:
The patent creates a universal modeling framework that can be applied across multiple machines and applications. The modular architecture with standardized interfaces allows the same model generation and validation tools to serve different machines, reducing the need for custom development while maintaining accuracy through data-driven adaptation to each specific application.
Solution Approach 2:
The patent uses template-based model structures that can be copied and adapted across different machines. Once a model framework is developed and validated for one application, it can be replicated for similar applications with minimal modification, reducing complexity while maintaining accuracy through parameter adjustment rather than complete redesign.
3Productivity
If sophisticated monitoring and control schemes are implemented, then process optimization capability is improved, but ease of operation and adaptability to new applications decrease
Solution Approach 1:
The patent implements dynamic model generation that automatically adapts to different applications and operating conditions. The system can dynamically select appropriate model structures, adjust parameters, and validate models based on the specific application context, making sophisticated control capabilities easily deployable across varying scenarios without manual reconfiguration.
Solution Approach 2:
The system performs self-validation and self-adjustment through automated model validation routines that assess model quality and suggest improvements without requiring expert intervention. This self-service capability maintains high optimization performance while simplifying operation for users who lack deep modeling expertise.
Data Source
AI summary
A modular analysis engine provided classification of variables and data in an industrial automation environment. The module may be instantiated upon receipt of an input data structure, such as containing annotated data for any desired variables related to the machine or process monitored and/or controlled. The data may be provided in a batch or the engine may operate on streaming data. The output of the module may be a data structure that can be used by other modules, such as for modeling, optimization, and control. The classification may allow for insightful analysis, such as for textual classification of alarms provided in the automation setting.