Industrial Analytics Architecture for Multi-Source Data Normalization
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Solution Overview
Problem
Industrial automation systems face challenges in effectively monitoring and analyzing data from diverse sources, leading to missed opportunities for collective analysis and insight into industrial operations, as data from various systems and devices is often recorded in different formats and formats, hindering comprehensive understanding and optimization of industrial processes.
Innovation Solution
A cloud-based data ingestion and analysis architecture that integrates and normalizes data from multiple sources, using a system that includes a device interface, data queuing, discovery, and metadata generation components to identify relationships between data items and generate metadata, allowing for scalable analytics across different levels of an industrial enterprise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple industrial automation systems is collected and analyzed collectively, then comprehensive insights and operational optimization are improved, but system complexity and data integration difficulty increase
Solution Approach 1:
The patent introduces a cloud-based data ingestion and analysis architecture that acts as an intermediary between multiple industrial automation systems. This intermediary layer collects, normalizes, and analyzes data from diverse sources without requiring direct integration between the source systems, thereby reducing system complexity while enabling comprehensive data analysis across the enterprise.
2Adaptability or versatility
If data from diverse sources is integrated and normalized, then comprehensive analysis capability is improved, but data processing complexity increases
Solution Approach 1:
The patent implements a universal data normalization layer that handles multiple data formats and sources through a single processing architecture. This multi-functional approach enables the system to adapt to diverse data sources while maintaining a consistent processing pipeline, thereby improving comprehensive analysis capability without proportionally increasing processing complexity.
3Speed
If analytics are performed at device level only, then real-time monitoring is improved, but collective insight and enterprise-wide optimization are limited
Solution Approach 1:
The patent implements a segmented analytics architecture where analysis occurs at multiple levels: device-level analytics provide real-time monitoring and immediate insights, while cloud-based analytics aggregate data across the enterprise to generate comprehensive insights. This segmentation allows the system to maintain real-time responsiveness at the device level while capturing collective insights at the enterprise level.
4Productivity
If enterprise-wide data analysis is implemented, then operational optimization is improved, but implementation scalability becomes challenging
Solution Approach 1:
The patent implements a dynamic, scalable architecture where analytics capabilities can be selectively deployed at different levels of the enterprise. The system allows organizations to start with device-level analytics and progressively expand to enterprise-wide analysis as needed, making implementation scalable and adaptable to varying organizational requirements and resource availability.
Data Source
AI summary
A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.