Automated Oscillation Root-Cause Diagnosis for Plant Control Loops
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Solution Overview
Problem
Industrial processes suffer from plant-wide oscillations caused by factors like faulty equipment, improper sensor placement, and control loop interactions, leading to equipment failures, energy inefficiencies, and product quality issues, necessitating an effective root-cause diagnosis system.
Innovation Solution
A system and method for automated root-cause diagnosis using spectral analysis, causality analysis, and connectivity strength to identify dominant frequencies, form asset groups, and determine root-cause assets and propagation paths based on operational data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to diagnose plant-wide oscillations, then diagnostic accuracy may be maintained, but the complexity and time consumption of the diagnosis process increases significantly
Solution Approach 1:
The diagnosis process is segmented into distinct automated stages: spectral analysis of operational data to identify dominant frequencies, causality analysis to determine connectivity strength between assets, and root-cause identification based on predefined thresholds. This segmentation transforms a complex manual diagnostic task into manageable automated components, reducing overall process complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated computational systems. Spectral analysis algorithms automatically process operational data to extract frequency information, while causality analysis computations replace manual assessment of connectivity between assets. This substitution eliminates the need for complex manual diagnostic procedures while preserving measurement precision.
2Extent of automation
If automated spectral analysis and causality analysis are implemented to diagnose root causes, then diagnostic speed and automation level improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary spectral analysis on operational data to identify dominant frequencies before conducting causality analysis. By pre-processing the data to extract frequency characteristics and grouping assets by common frequencies, the system reduces the complexity of subsequent causality analysis. This preliminary action organizes the data structure to facilitate faster automated processing while maintaining diagnostic accuracy.
Solution Approach 2:
The patent applies partial action by focusing computational resources on identifying only the dominant frequencies and their associated causality relationships, rather than analyzing all possible frequency components. This selective approach achieves sufficient automation for practical diagnostic purposes without requiring excessive computational complexity, balancing automation level with processing requirements.
3Measurement precision
If connectivity strength thresholds are used to identify root-cause assets, then the precision of root-cause identification improves, but the complexity of analyzing causality relationships between assets increases
Solution Approach 1:
The patent transforms the complex multivariate causality analysis problem into a simpler univariate threshold comparison problem by changing the parameter from full causality matrix analysis to connectivity strength threshold evaluation. Assets are grouped by dominant frequency, and root-cause identification is achieved by comparing connectivity strength against predefined thresholds, significantly reducing analytical complexity while maintaining identification precision.
Solution Approach 2:
The system extracts only the critical connectivity strength parameter from the full causality relationships between assets. By focusing on this single extracted parameter and comparing it against thresholds, the system avoids the complexity of analyzing all causality relationships while maintaining precise root-cause identification. This extraction approach simplifies the diagnostic process to essential elements only.
Data Source
AI summary
The present disclosure discloses a system and a method for the root-cause diagnosis of plant-wide oscillations based on process data. According to an embodiment, the method includes automated processing of data, an automated approach for selecting relevant and clustering common control loops having oscillations. The method further includes a causality analysis-based automated approach for identifying root causes and propagation paths. The disclosed system and method improve overall process performance and production efficiency by efficiently detecting and diagnosing the root causes of the plant-wide oscillations and addressing associated issues. The system further enhances operational efficiency, mitigates safety risks, and minimizes production losses and costs.