Cloud-Based Operator Training for Industrial Automation Performance
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Solution Overview
Problem
Industrial automation systems often operate less efficiently when less experienced or less knowledgeable operators interact with them, leading to suboptimal performance and potential safety risks.
Innovation Solution
A cloud-based performance enhancement service analyzes user interactions with industrial automation systems across multiple enterprises to identify correlations between user actions and system performance, determining preferred actions and generating training modules to improve user efficiency and automate optimal operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operators with varying experience levels work with industrial automation systems, then operational flexibility is maintained, but system performance and safety deteriorate due to less experienced operators performing less efficiently
Solution Approach 1:
The system captures user actions from multiple industrial customers and uses performance enhancement components to analyze this data, generating feedback in the form of training modules and best practice recommendations. This feedback loop continuously improves operator performance by learning from aggregated experiences across the customer base.
Solution Approach 2:
The system identifies and copies preferred user actions from experienced operators across different customers. These copied actions are then used to create training modules and recommendations that can be applied to less experienced operators, effectively transferring knowledge without direct human intervention.
2Productivity
If cloud-based data from multiple customers is analyzed, then training effectiveness and system performance improve, but data processing complexity and infrastructure requirements increase
Solution Approach 1:
The cloud-based platform serves multiple functions: capturing user actions from diverse customers, storing data, analyzing performance patterns, generating training modules, and delivering recommendations. This multi-functional system consolidates what would otherwise require separate systems into a unified platform.
Solution Approach 2:
The system automatically captures user actions through integration with industrial automation systems, performs analysis without manual intervention, and generates training content autonomously. The performance enhancement components self-organize to identify preferred actions and create improvement recommendations without requiring dedicated data scientists for each customer.
3Reliability
If user actions are captured and analyzed across multiple industrial customers, then best practices can be identified and applied, but data collection and processing time increase
Solution Approach 1:
The system continuously captures and stores user actions in the cloud as they occur during normal operations. This preliminary data collection happens in the background without interrupting production, so when analysis is needed, the data is already prepared and available for immediate processing.
Solution Approach 2:
The data collection process operates continuously during system operation, maintaining an ongoing record of user actions. This continuous capture ensures that preferred actions are identified from real operational data rather than discrete sampling, improving reliability while minimizing additional time requirements.
Data Source
AI summary
A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. The service employs a performance enhancement component that analyzes the data to facilitate determining correlations between certain user interactions and favorable performance of an industrial automation system, determining user interactions that are less favorable or unsafe, determining alternative actions that a user can take to achieve a same or similar preferred operational result, generating recommendations relating to the alternative actions, determining or designing components or techniques that can automate a preferred user action, determining improved user assignments in connection with the industrial automation system, and/or generating training modules or presentations based on preferred user actions that can be used to train users to more efficiently interact with an industrial automation system to achieve improved system performance.