Digital Machine Model for Multi-Source Component Status Detection
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Solution Overview
Problem
Current methods for determining the status of machine components are inadequate in providing comprehensive and accurate assessments, especially in linking component manufacturer, machine manufacturer, and operator data for predictive maintenance and fault detection.
Innovation Solution
A method and system that utilize a digital machine model to automatically determine the status of machine components by integrating component manufacturer data, machine manufacturer data, and machine operator data, enabling improved status determination and remote maintenance through a digital twin model, which can be visualized and accessed centrally or locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources (component manufacturer data, machine manufacturer data, machine operator data) are integrated to determine component status, then measurement precision and reliability of status determination is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple data sources (component manufacturer data, machine manufacturer data, and machine operator data) into a unified digital machine model. This consolidation allows comprehensive status determination by integrating information from different levels (component, machine, operation) while managing complexity through structured data organization and standardized processing methods.
Solution Approach 2:
The digital machine model serves as a universal platform that handles multiple functions: storing component specifications, tracking machine operations, analyzing status data, and supporting predictive maintenance. This multi-functional approach eliminates the need for separate systems for each data type, thereby improving measurement precision without proportionally increasing device complexity.
2Reliability
If comprehensive data from multiple sources is collected and linked, then predictive maintenance capability and reliability are improved, but loss of time for data processing and system setup increases
Solution Approach 1:
The patent implements preliminary action by pre-structuring the digital machine model with defined data categories and relationships before actual status determination. Component manufacturer data, machine manufacturer data, and machine operator data are organized in advance with predetermined linkages, enabling rapid data retrieval and processing when status assessment is needed, thus reducing actual processing time while maintaining comprehensive data analysis.
Solution Approach 2:
The digital machine model creates a virtual copy or representation of the physical machine's data structure and relationships. This digital twin approach allows comprehensive data analysis to be performed on the copied model without requiring simultaneous access to all physical data sources, significantly reducing data collection time while maintaining predictive maintenance accuracy.
3Ease of operation
If a digital machine model with structured part lists and memory areas is implemented, then status determination and remote maintenance capability are improved, but device complexity and implementation cost increase
Solution Approach 1:
The digital machine model acts as an intermediary between the physical machine and remote maintenance operations. By creating a structured digital representation with memory areas for each component, the system enables remote access and analysis without requiring direct physical interaction with the complex machine infrastructure. This intermediary layer simplifies remote maintenance operations while the modular structure manages implementation complexity.
4Productivity
If data is stored in structured memory areas associated with each machine component, then data retrieval efficiency and status determination speed are improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent segments the data storage system into structured memory areas, with each memory area associated with a specific machine component. This segmentation allows rapid retrieval of component-specific data by directly accessing the relevant memory area without searching through entire data sets. The modular segmented structure improves productivity while managing storage complexity through organized data partitioning.
Data Source
AI summary
A method for determining a status of one of multiple machine components of a machine on the basis of a digital machine model, wherein the digital machine model describes the multiple machine components, includes the steps of: determining component manufacturer data of the multiple machine components; determining machine manufacturer data of the multiple machine components; determining machine operator data of the multiple machine components; and determining the status of the one of the multiple machine components by linking the determined component manufacturer data, the determined machine manufacturer data and the determined machine operator data.