Process Element Alignment Map for Fault Source Identification
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Solution Overview
Problem
Current process control systems in plants lack the ability to automatically determine the alignment of process elements and identify sources of faults or variations without user input, leading to incomplete and erroneous data, which hampers troubleshooting and predictive modeling.
Innovation Solution
The system automatically generates a process element alignment map by extracting data from multiple sources, determining the order of process elements, and using this map to identify upstream elements contributing to faults or variations, without relying on user-generated inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user input is required to identify fault sources and process element alignments, then the system can operate with existing data processing capabilities, but the completeness and accuracy of data analysis deteriorates due to incomplete and erroneous user-generated inputs
Solution Approach 1:
The system performs self-service by automatically determining process element alignments and identifying fault sources using data from process control systems without requiring user input. The processor executes instructions to autonomously analyze process data, establish element relationships, and pinpoint fault origins, eliminating dependency on user-generated inputs while maintaining high accuracy in fault identification
Solution Approach 2:
The system performs preliminary action by pre-establishing process element alignments and relationships before fault occurrence. The processor continuously maintains an updated understanding of process element interconnections and data flows, so when a fault occurs, the system can immediately leverage this pre-established knowledge to rapidly identify fault sources without requiring user input or time-consuming analysis
2Productivity
If automatic determination of process element alignment is implemented, then troubleshooting efficiency and data completeness improve, but system complexity increases due to multiple data sources and processing requirements
Solution Approach 1:
The system applies universality by designing a multi-functional processor that can handle diverse data sources (process control systems, historians, databases) and perform multiple functions (data extraction, alignment determination, fault source identification) within a single integrated platform. This universal approach consolidates complexity into one system rather than requiring separate specialized systems for each function
Solution Approach 2:
The system uses an intermediary approach by introducing a processor that acts as a mediator between multiple data sources and the analysis functions. The processor receives data from various sources, standardizes and integrates it, and then provides unified output for fault analysis, simplifying the overall system architecture by centralizing data integration and processing logic in a single intermediary component
3Reliability
If user-generated inputs are relied upon for fault analysis, then system implementation is simpler, but the reliability of fault source identification deteriorates due to incomplete and erroneous data
Solution Approach 1:
The system achieves self-service by automatically extracting data from process control systems and determining process element alignments without relying on user-generated inputs. The processor autonomously identifies fault sources by analyzing actual process data and established element relationships, ensuring reliable and accurate fault identification while eliminating errors associated with manual data entry
Solution Approach 2:
The system implements feedback by continuously monitoring process data and using this information to refine and update process element alignments and fault source identifications. The processor analyzes actual process behavior and uses this feedback to improve the accuracy and reliability of fault detection, creating a self-correcting system that becomes more reliable over time
Data Source
AI summary
Techniques for automatically determining, without user input, one or more sources of a variation in the behavior of a target process element operating to control a process in a process plant include using a process element alignment map to determine process elements upstream of the target process element in the process; performing a data analysis on data corresponding to the upstream elements with respect to the target element to determine behavior time offsets, strengths of impact, and impact delays; and determining the source(s) based on the data analysis outputs. Techniques may include automatically defining the process element alignment map by obtaining and processing data from a plurality of diagrams or data sources of the process and/or plant. Furthermore, the techniques may be performed during plant run-time by any high-volume, high density device such as centralized or embedded big data appliances, controllers, field or I/O devices, and/or by an unsupervised application.