Local Target Variable Modeling for Industrial Automation Control
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently monitoring and diagnosing complex processes due to the increasing volume and complexity of data, leading to inefficiencies in real-time performance monitoring and diagnostics, and there is a need for localized solutions that do not rely on cloud computing resources.
Innovation Solution
A local control system with multiple modules, including an AI module, that analyzes raw data without context to identify influential subsets, models behavior, and adjusts operations autonomously, while also encrypting data for security, without transmitting sensitive information outside the local network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing resources are used for data analysis, then processing power and storage capacity are improved, but data security and transmission time are worsened
Solution Approach 1:
The patent segments the control system into multiple distributed modules (edge devices, local controllers, cloud services) that can independently process data locally while selectively transmitting only essential information to the cloud. This segmentation enables local processing to maintain data security while distributing computational tasks across the segmented architecture.
Solution Approach 2:
The patent introduces an intermediary layer (edge computing devices and local controllers) between the automation devices and cloud computing resources. This intermediary performs preliminary data processing, filtering, and analysis locally, transmitting only processed results or critical data to the cloud, thereby reducing transmission time and enhancing data security while still utilizing cloud computing power when needed.
2Measurement precision
If all datasets are analyzed for monitoring, then measurement completeness is improved, but processing time and computational complexity are worsened
Solution Approach 1:
The patent extracts and identifies only the most influential subsets of datasets that have the greatest impact on target variables, rather than analyzing all available data. This extraction approach maintains monitoring completeness for critical parameters while significantly reducing the volume of data requiring processing, thereby decreasing processing time and computational complexity.
Solution Approach 2:
The patent applies partial action by focusing computational resources on analyzing only the essential subsets of data that are sufficient for effective monitoring and control decisions. Rather than performing exhaustive analysis of all datasets, the system identifies and processes the critical portion needed to achieve monitoring objectives, reducing overall processing time while maintaining adequate monitoring completeness.
3Adaptability or versatility
If more modules are added to the control system, then functional versatility is improved, but system complexity and data transmission overhead are worsened
Solution Approach 1:
The patent designs control modules with multi-functional capabilities, where each module can perform multiple tasks (data collection, local processing, encryption, selective transmission, and control execution). This universality allows the system to achieve high functional versatility without proportionally increasing the number of modules, thereby limiting the growth of system complexity and data transmission overhead.
4Reliability
If data is encrypted for security, then data protection is improved, but processing speed and communication efficiency are worsened
Solution Approach 1:
The patent extracts and transmits only essential processed information rather than encrypting and transmitting all raw data. By processing data locally to extract key insights and transmitting only these essential results, the system maintains strong data protection through selective encryption while minimizing the impact on processing speed and communication efficiency.
Data Source
AI summary
A method for operating an industrial automation system may include receiving, via a first module of a plurality of modules in a control system, a plurality of datasets via at least a portion of the plurality of modules. The plurality datasets may include raw values without context regarding the plurality datasets. The method may then include identifying a subset of the plurality of datasets that influences a value of a target variable by analyzing the data without regard to the context, modeling a behavior of the target variable over time based on the subset of the plurality of datasets, and adjusting one or more operations of an automation device based on the model.