Cloud Analytics Gateway for Industrial Automation Correlation
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Solution Overview
Problem
Industrial automation systems lack effective methods for real-time data analysis and correlation to enhance operational performance, leading to inefficiencies and potential deviations from optimal performance.
Innovation Solution
A cloud-based analytics system that collects and analyzes data from industrial automation systems, determining correlations between system components, extrinsic events, and employee performance to provide recommendations for improving system performance and preventing deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based data collection and analysis is implemented, then operational efficiency and system performance are improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent introduces a cloud gateway as an intermediary component that bridges the industrial automation system and the cloud-based analytics platform. This gateway handles data collection, preprocessing, and transmission to the cloud, thereby reducing the complexity burden on the core automation system while enabling advanced analytics capabilities through the cloud infrastructure.
Solution Approach 2:
The patent moves data processing and analytics from the traditional on-premise dimension to the cloud dimension, creating a hybrid architecture. This dimensional shift allows complex analytics to be performed remotely while the local system maintains its core control functions, effectively distributing complexity across different spatial and operational dimensions.
2Loss of time
If real-time data analysis is performed locally, then response time is reduced, but computational resource requirements and system cost increase
Solution Approach 1:
The patent implements a partial local processing approach where critical real-time data is analyzed locally for immediate responses, while less time-sensitive data is transmitted to the cloud for comprehensive analysis. This partial action strategy achieves necessary response times without requiring full local computational capacity, thereby reducing resource requirements.
3Measurement precision
If comprehensive data collection from multiple sources is implemented, then analytical accuracy is improved, but data management complexity and storage requirements increase
Solution Approach 1:
The patent segments data collection into distinct modules that target specific data sources and types (process data, equipment data, environmental data, etc.). Each module collects and pre-processes data from its designated source, then transmits structured data to the cloud platform. This segmentation reduces overall data management complexity while maintaining comprehensive data coverage for accurate analytics.
Data Source
AI summary
A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations. The analytics engine also can determine when there is a deviation or potential of deviation from desired system performance by an industrial asset or employee, and provide a notification, a recommendation, or an instruction to rectify or avoid the deviation.