Operations Management System for Real-Time Fault Prediction
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Solution Overview
Problem
Current process control systems rely on offline tools for fault detection and correction, which are reactive and cannot remediate quality issues in real-time, leading to the production of products with quality issues until the process is corrected.
Innovation Solution
An operations management system (OMS) that provides in-process fault detection, analysis, and correction by predicting faults based on process control information, allowing for immediate corrective actions to maintain product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline tools are used for fault detection and correction, then the system can identify process issues, but the correction cannot be applied in real-time leading to production of out-of-spec products
Solution Approach 1:
The system performs preliminary fault prediction by analyzing process control information and comparing it against historical data and models before the fault actually occurs or impacts product quality. This allows operators to take corrective action in advance, preventing the production of out-of-spec products rather than detecting and correcting after the fact.
Solution Approach 2:
The system implements continuous feedback by monitoring process control information in real-time, comparing current state against expected parameters, and providing immediate alerts when deviations are detected. This closed-loop feedback mechanism enables rapid response and correction, transforming the traditional offline reactive approach into an online proactive system.
2Reliability
If process monitoring is performed continuously, then real-time fault detection is enabled, but the complexity of the monitoring system increases
Solution Approach 1:
The system introduces intelligent software intermediaries including predictive models, data analysis algorithms, and pattern recognition systems that act as mediators between raw process control information and fault detection. These intermediaries process and interpret data, transforming complex continuous monitoring into manageable analytical tasks that maintain high reliability without proportionally increasing system complexity.
Solution Approach 2:
The monitoring system is segmented into modular functional components including data acquisition modules, predictive modeling modules, analysis modules, and alerting modules. This segmentation allows each component to be optimized independently and facilitates easier maintenance and scaling, reducing the perceived complexity while maintaining comprehensive monitoring capabilities.
Data Source
AI summary
Example methods, apparatuses and systems to correlate candidate factors to a predicted fault in a process control system are disclosed. Techniques may include obtaining a value associated with a particular factor corresponding to a process, and predicting a fault based on the value. A set of candidate factors corresponding to the predicted fault may be determined, and a correlation between the predicted fault and at least one factor from the set may be displayed. Different sections of the display may respectively correspond to the predicted fault and to the at least one factor, and the correlation may be indicated by time aligning the different sections. Modifications to one displayed section may result in automatic modification of other sections to maintain the correlation. A user may select one or more candidate factors to be displayed, and may indicate a particular point of a particular section to obtain additional details.