Self-Correcting Multivariate Analysis for Process Maturation
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Solution Overview
Problem
Multivariate analysis (MVA) models become less effective in detecting faults in dynamic processes due to process maturation over time, as they fail to account for aging, leading to false alarms and the need for frequent model updates, which can be costly and prone to human error.
Innovation Solution
A self-correcting MVA method that identifies process parameters correlated with maturation, using these 'initial conditions' to adjust control thresholds and scale the reference model, allowing it to differentiate between normal operating conditions and changes caused by maturation, thereby maintaining robustness without frequent updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static MVA reference model is used for fault detection, then the model structure is simple and easy to implement, but the model becomes ineffective over time due to process maturation and aging
Solution Approach 1:
The patent transforms the static MVA reference model into a dynamic model that automatically adapts to process maturation. The model uses a maturation function based on monitored parameters to dynamically adjust the reference model, allowing it to evolve with the process while maintaining structural simplicity. This resolves the contradiction by making the model structure dynamically adaptable rather than statically fixed.
Solution Approach 2:
The patent changes the parameters of the reference model based on process maturation. By introducing a maturation function that modifies model parameters (such as mean vectors and covariance matrices) based on monitored parameter trends, the model maintains effectiveness over time. This allows the model to account for process aging without requiring complete model reconstruction.
2Reliability
If the MVA reference model is frequently updated to account for process maturation, then the fault detection accuracy is maintained, but the system requires significant human intervention and maintenance time
Solution Approach 1:
The patent implements a self-updating MVA reference model that automatically adapts to process maturation without human intervention. The model uses a maturation function that automatically adjusts parameters based on monitored process data, eliminating the need for manual model updates. This resolves the contradiction by making the model self-maintaining while preserving detection accuracy.
Solution Approach 2:
The patent introduces a feedback mechanism where the MVA model continuously monitors process parameters and uses this information to automatically update itself. The maturation function receives feedback from monitored parameters and adjusts the reference model accordingly, creating a closed-loop system that maintains accuracy without external intervention.
3Reliability
If the MVA reference model is periodically updated, then the model remains robust to process drift, but the frequent updates increase device complexity and potential for human error
Solution Approach 1:
The patent creates a self-updating model that automatically tracks process maturation using a maturation function. Instead of requiring periodic manual updates or complex update mechanisms, the model autonomously adapts to drift by continuously monitoring parameter trends and adjusting its reference values. This simplifies the overall system complexity while maintaining robustness.
4Reliability
If a reference model is created from process samples across a single cycle, then the model accounts for process maturation patterns, but events defined by excursions may not be identified as defects
Solution Approach 1:
The patent uses a dynamic maturation function that continuously adapts the reference model to current process conditions. Instead of using a static model based on historical cycles, the model dynamically adjusts to the current maturation state while maintaining the ability to detect anomalies. This allows the model to be adaptive to maturation while preserving defect detection precision through real-time comparison.
Data Source
AI summary
A method and apparatus for process monitoring are provided. Process monitoring includes (i) generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment; (ii) designating at least one of the monitored parameters as being correlated to maturation of the process environment; (iii) collecting current process data corresponding to the monitored parameters, including the at least one designated parameter; and (iv) scaling the multivariate reference model based on the current process data of the at least one designated parameter to account for maturation of the process environment. The method further includes generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data; and comparing the current process metrics to the scaled reference model to determine whether the current state of the process environment is acceptable.