Equipment Impact Lists for Industrial Sub-Process Dependency Analysis
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Solution Overview
Problem
Industrial plants and processes are complex systems where identifying the functional dependencies between equipment elements is challenging, making it difficult to maintain and improve them effectively.
Innovation Solution
A method involving a topology model graph to select and traverse equipment elements, using a traversing strategy to create an impact list of elements affecting an industrial sub-process, with the option to control or influence material, energy, or information flow, and utilizing machine learning for improved analysis and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive analysis of all equipment elements in industrial plants is performed to identify functional dependencies, then the completeness of dependency identification is improved, but the complexity of analysis and time consumption increase significantly
Solution Approach 1:
The patent segments the complex industrial plant system into smaller functional sub-processes and groups equipment elements into impact groups based on their functional dependencies. This segmentation allows analysis to be performed on manageable subsets rather than the entire plant system at once, reducing analysis complexity while maintaining completeness through systematic coverage of all segments.
Solution Approach 2:
The patent extracts and focuses analysis on equipment elements that have a significant impact (above a threshold affecting degree) on selected sub-processes. By taking out only the relevant equipment elements from the complete set and analyzing their functional dependencies, the method achieves complete dependency identification for critical elements without the computational burden of analyzing all elements equally.
2Measurement precision
If all equipment elements are analyzed to identify functional dependencies, then the accuracy of maintenance decisions is improved, but the time required for analysis increases
Solution Approach 1:
The patent applies local quality by focusing detailed analysis on specific equipment elements that have a high affecting degree on selected sub-processes, rather than uniformly analyzing all elements. This allows accurate maintenance decisions to be made for critical equipment while reducing time spent on elements with minimal impact, achieving a balance between decision accuracy and analysis time.
Solution Approach 2:
The patent changes the parameter of analysis scope by dynamically adjusting which equipment elements are included based on their affecting degree threshold. By modifying this parameter, the system can quickly adapt between comprehensive analysis (higher threshold) and focused analysis (lower threshold), enabling accurate maintenance decisions to be made within varying time constraints.
3Quantity of substance
If the complete list of all equipment elements is provided for machine learning training, then the comprehensiveness of training data is improved, but the computational resources and training time increase
Solution Approach 1:
The patent applies partial action by providing a filtered list of equipment elements that meet a minimum affecting degree threshold for machine learning training, rather than including all elements. This partial inclusion of relevant elements provides sufficient comprehensiveness for effective training while significantly reducing the computational resources and training time required compared to using the complete set of all equipment elements.
4Productivity
If detailed functional dependencies of all equipment elements are identified, then the ability to improve plant operations is enhanced, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the affecting degrees and functional dependency relationships of equipment elements before actual maintenance or improvement activities. This preliminary processing organizes the data in a structured format that can be quickly queried and applied during operations, enhancing the ability to improve plant operations while managing data processing complexity through advance preparation.
Data Source
AI summary
A system and method provides an impact list of affecting equipment elements that affect an industrial sub-process. The method comprises the steps of selecting, in a topology model, the sub-process, wherein the sub-process is an equipment element that is a part of an industrial plant or process, and wherein the topology model is a graph, whose nodes represent equipment elements and whose edges represent interconnections between the equipment elements; traversing the nodes of the topology model, wherein the traversing starts from the selected sub-process and uses a traversing strategy; and for each of the at least one equipment elements, if the equipment element affects the industrial sub-process by an affecting degree greater than a first predefined affecting degree, adding the equipment element to the impact list of affecting equipment elements.