Automatic Data Collection Configuration for Industrial Monitoring
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Solution Overview
Problem
Large-scale industrial process control and automation systems require extensive time and resources to configure data collection systems, leading to non-competitive costs and delays in monitoring system availability.
Innovation Solution
A system and method for automatic configuration of data collection systems and schedules, which involves discovering assets, cross-referencing them with a collection model and application requirements to determine collectable data, and automatically generating a collection schedule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration of data collection system is performed by site experts, then data collection requirements are met, but configuration time is excessively long (several weeks) and costs are high
Solution Approach 1:
The system performs self-configuration by automatically discovering assets, identifying data collection requirements, and generating collection schedules without human intervention. The automated system serves itself by cross-referencing asset metadata with collection models and application requirements to configure the entire data collection system independently.
Solution Approach 2:
The patent replaces the manual mechanical process of expert configuration with an automated computational system. Instead of site experts manually identifying assets and configuring collection schedules, the system uses automated asset discovery, metadata analysis, and algorithmic schedule generation to substitute human labor with computational processes.
2Reliability
If comprehensive data collection is implemented for all assets, then monitoring completeness is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system applies different data collection configurations to different assets based on their specific characteristics, types, and associated application requirements. Instead of uniform collection across all assets, the system tailors collection schedules and parameters to local needs by cross-referencing asset metadata with application-specific requirements.
Solution Approach 2:
The system dynamically adjusts data collection parameters such as sampling rates, collection intervals, and data types based on asset characteristics and application requirements. The automated configuration modifies these parameters systematically by analyzing the relationship between asset metadata and application needs, optimizing collection efficiency.
3Productivity
If automated asset discovery is performed, then configuration speed is improved, but initial system setup complexity increases
Solution Approach 1:
The system performs preliminary asset discovery and metadata collection automatically during initial system deployment. By pre-identifying all assets and their characteristics before configuration begins, the system eliminates the need for manual asset inventory and prepares all necessary information in advance for automated schedule generation.
Solution Approach 2:
The automated configuration system performs multiple functions including asset discovery, metadata analysis, requirement matching, and schedule generation within a single integrated process. This multi-functional approach consolidates what would otherwise require separate manual steps into one automated workflow, reducing overall setup complexity despite the advanced capabilities required.
Data Source
AI summary
A method includes discovering one or more assets associated with a system. The method also includes determining first data that could be collected from each of the one or more assets by cross-referencing the one or more assets with a collection model. The method further includes determining second data that is to be collected from each of the one or more assets by cross-referencing the first data with requirements of one or more applications that use data from the one or more assets. In addition, the method includes automatically generating a schedule for collection of the second data from the one or more assets.