Automatic Asset Data Classification for Process Control Configuration
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Solution Overview
Problem
Large-scale industrial process control and automation systems require significant human effort and time to configure data collection systems, leading to data entry errors and increased costs due to the need for explicit asset classification and context specification.
Innovation Solution
A system and method for automatic data classification, where a processing device accesses predefined classifications, receives user input for customization, generates customized classifications, stores them, collects asset data, and processes it according to the associated policies, thereby reducing manual effort and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual configuration and explicit asset classification are used, then data collection systems can be set up, but significant human effort and time are required leading to data entry errors and increased costs
Solution Approach 1:
The system enables self-service by automatically discovering assets and classifying them based on predefined policies without requiring manual intervention. The data collection system autonomously identifies assets, determines their types, and applies appropriate classifications and contexts, eliminating the need for expert configuration time while reducing errors.
Solution Approach 2:
The system implements preliminary action by pre-defining data classifications and policies before asset discovery. The predefined policies contain classification rules and contexts that are prepared in advance, allowing the system to automatically apply them during asset discovery and data collection, thereby reducing configuration time and effort.
2Reliability
If manual asset identification and classification are performed, then data collection can be configured, but numerous data entry errors occur and costs increase
Solution Approach 1:
The system performs self-service by automatically classifying assets based on their identified types and predefined policies. This eliminates manual data entry operations that cause errors, as the system autonomously determines classification and context, thereby improving reliability while managing complexity through automated rule-based processing.
Solution Approach 2:
The system implements feedback mechanisms where asset data is collected and automatically classified according to predefined policies, with the ability to refine and update classifications based on collected information. This closed-loop approach improves accuracy by validating classifications against established rules and allowing for continuous refinement.
Data Source
AI summary
A method includes accessing, from a data store, at least one predefined data classification for asset data associated with multiple assets in an industrial process control system, wherein the at least one predefined data classification is associated with one or more first policies, wherein the data store stores a plurality of data classifications for asset data. The method also includes receiving user input of a customization to the at least one predefined data classification to generate at least one customized data classification associated with one or more second policies. The method further includes storing the at least one customized data classification in the data store. The method also includes collecting asset data from at least one of the multiple assets. The method further includes processing the collected asset data according to the one or more second policies associated with the at least one customized data classification.