Industrial Asset Regime Modeling for Process Optimization
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Solution Overview
Problem
Traditional techniques for regime identification in industrial data, such as Mahalanobis distance and principal component analysis, fail to consider the temporal relationships in industrial data, making them ineffective for process optimization and predictive maintenance in dynamic industrial assets like reactors and gas turbines.
Innovation Solution
A processor-implemented method and system that preprocesses data from industrial assets by removing redundancy, unifying sampling frequencies, and integrating variables, then uses hierarchical clustering to identify regimes of operation, compute regime similarity scores, and optimize key performance parameters, while detecting anomalies and estimating remaining useful life through adaptive learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional techniques such as Mahalanobis distance and principal component analysis are used for regime identification, then the analysis can be performed with simpler methods, but the temporal relationship in industrial data is not considered making the techniques ineffective
Solution Approach 1:
The patent replaces traditional statistical methods (Mahalanobis distance, PCA) with a hidden Markov model-based approach that incorporates temporal relationships. This substitution transforms the analysis from static pattern recognition to dynamic temporal sequence modeling, resolving the contradiction by accepting increased model complexity to achieve reliable regime identification in dynamic industrial processes
Solution Approach 2:
The patent changes the fundamental parameters of the analysis by introducing hidden states and transition probabilities that capture temporal dynamics. Instead of analyzing data points independently, the system models the temporal evolution of process regimes through state transition matrices, enabling effective regime identification that respects the temporal structure of industrial data
2Adaptability or versatility
If multiple industrial assets are monitored with dynamic behavior and multiple regimes of operation, then comprehensive monitoring coverage is achieved, but it becomes important and complex to identify the regime of operation of the units
Solution Approach 1:
The patent creates a universal regime identification framework based on hidden Markov models that can be applied across multiple different industrial assets (gas turbines, reactors, combustion engines) despite their different dynamics. The same probabilistic modeling approach handles diverse operating regimes and asset types, achieving versatility without proportional increases in complexity
Solution Approach 2:
The patent uses regime sequences derived from historical data as templates to identify and classify current operating states. By copying and matching observed data patterns against known regime sequences, the system efficiently identifies regimes across multiple assets without requiring complex asset-specific modeling for each unit
3Productivity
If regime identification is performed without considering temporal relationships, then the processing can be done faster and simpler, but the identification is inaccurate for dynamic industrial assets
Solution Approach 1:
The patent performs preliminary segmentation of data into potential regime sequences and pre-computes transition probabilities from historical data. This preparation allows the actual regime identification to proceed efficiently by matching against pre-established temporal patterns, maintaining both speed and accuracy in dynamic asset monitoring
Data Source
AI summary
This disclosure relates to a method and system for regime-based process optimization of various assets of industrial manufacturing and process plants or units. Operating regimes of assets are identified and a regime similarity score of each asset is computed from regime sequences of integrated industrial data and compared with a given threshold to identify regimes of operation. Operating regimes are matched with regimes of regime database and industrial assets and similar operating regimes are group together. Process optimization is carried out for each group of industrial assets to identify optimum settings in order to maximize output or minimize loss/cost considering process and equipment constraints. Anomalies are analyzed in the unmatched operating regimes and a diagnosis is carried out to identify the root cause for any detected anomalies. Remaining useful life of components in the assets with unmatched operating regimes is estimated to ensure component reliability and to prevent failure.