Modular System Analysis Device for Industrial Control Troubleshooting
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Solution Overview
Problem
Current industrial control systems require complex and expensive troubleshooting methods that are not reusable and often rely on empirical analysis, making it difficult to quickly diagnose issues in automated control systems, especially in industrial installations.
Innovation Solution
A method and device using a virtual system model that identifies characteristic variables and influent variables to create a discordant variables list, allowing for rapid diagnosis by filtering suspect variables and predicting system states, enabling efficient and reusable analysis without disrupting the operating programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a troubleshooting application is integrated into the operating program for each machine, then the system can detect deviations and disruptions, but the application becomes expensive, complex, and not reusable
Solution Approach 1:
The troubleshooting application is segmented into separate, reusable modules that can be independently developed and applied to different machines. The analysis device is divided into distinct functional components including a model execution unit, a comparison unit, and a suspect variable identification unit, allowing modular deployment and reuse across multiple systems without requiring complete program integration for each machine
Solution Approach 2:
The troubleshooting application is designed as a universal, reusable module that can be applied across multiple different machines and systems. The analysis device uses a standardized model execution and comparison mechanism that can analyze various industrial installations controlled by programmed or wired logic automatic control systems, eliminating the need to develop custom troubleshooting applications for each individual machine
2Adaptability or versatility
If standard fault-finding modules are used, then reusability is improved, but the solution becomes excessively expensive and cannot handle non-standard conditions
Solution Approach 1:
The analysis device dynamically adjusts its analysis parameters and model configurations based on the specific system being analyzed. The model execution unit can modify simulation parameters, time constants, and variable thresholds to adapt to different industrial installations and their specific operating conditions, allowing the same reusable device to handle both standard and non-standard conditions without requiring custom development
Solution Approach 2:
The analysis device introduces an intermediary model execution layer between the reusable standard modules and the specific system being analyzed. This model layer acts as a mediator that translates standard fault-finding capabilities into system-specific analysis, allowing the reusable device to handle non-standard conditions by adjusting the model parameters rather than requiring custom code for each scenario
3Measurement precision
If physical action is taken to reach suspect areas, then fault location can be confirmed, but the operation becomes long and complex
Solution Approach 1:
The analysis device replaces physical inspection and manual testing with automated computational analysis. The model execution unit simulates system behavior and the comparison unit automatically identifies discrepancies, eliminating the need for technicians to physically access and test suspect components. The suspect variable identification unit then provides precise digital location information, substituting mechanical exploration with intelligent algorithmic analysis that rapidly pinpoints faults without physical intervention
4Ease of operation
If empirical analysis and experience are used to detect deviations, then simple systems can be monitored, but complex industrial installations cannot achieve rapid and efficient diagnosis
Solution Approach 1:
The analysis device enables self-service automated diagnosis by executing system models and automatically comparing predicted behavior with actual sensor data. The device independently identifies discordant variables and suspects without requiring expert human intervention or empirical analysis, allowing complex industrial installations to perform rapid self-diagnosis. The suspect variable identification unit automatically ranks potential faults, providing actionable insights without needing experienced technicians to interpret results
Data Source
AI summary
A device and a method for the analysis and troubleshooting of a system, based on the use of a model of the system, with application in particular in the area of industrial installations controlled by automatic control systems of the programmed or wired logic type. The method includes a stage for initialisation of the model, and a stage for the creation of a list of discordant variables whose value in the system differs from that predicted by the model. For each of the variables belonging to a discordance list, an initial list of suspect variables, suspected of having generated the discordant value, is created, and then a restricted list of suspect variables is obtained by filtration of the initial list.