Asset Management Workflow With Sensor Mapping and ML Prediction
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Solution Overview
Problem
Existing predictive analytics solutions for asset management in industrial environments lack customization for specific domains, fail to automatically determine required sensors, lack seamless integration of new assets, and lack the ability to scale and integrate domain knowledge, leading to low accuracy and reliability.
Innovation Solution
A system and method for managing assets that includes selecting relevant assets based on user requirements, mapping them to sensing units, determining performance indicators, and using machine learning models to predict outcomes, with automatic integration of new assets and updating of knowledge bases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate systems are used to manage different asset types (securities, commodities, real estate), then each asset type can be managed with specialized functionality, but the overall system complexity increases and coordination between assets becomes difficult
Solution Approach 1:
The patent combines multiple asset management systems (securities, commodities, real estate) into a single integrated system. The central processing unit receives instructions and distributes them across different asset classes through unified communication channels, eliminating the need for separate standalone systems while maintaining specialized management capabilities for each asset type.
Solution Approach 2:
The system employs universal communication protocols and standardized data structures that enable a single system to handle multiple asset types. The communication module can translate and route instructions across different asset classes using common interfaces, making the system versatile without requiring separate specialized systems for each asset type.
2Loss of information
If detailed tracking and monitoring is implemented for all asset transactions, then transaction transparency and control are improved, but data processing time and system resource consumption increase
Solution Approach 1:
The system pre-establishes communication channels and data structures for tracking transactions across all asset types. By preparing the infrastructure in advance with standardized formats and pre-configured routing paths, the system can process transaction data efficiently without requiring complex real-time setup, thus maintaining complete tracking while reducing processing time.
3Productivity
If real-time communication and coordination between multiple assets is implemented, then portfolio optimization and risk management are improved, but communication overhead and system resource usage increase
Solution Approach 1:
The patent introduces a central processing unit and standardized communication protocols as intermediaries that coordinate between different asset management modules. This intermediary layer enables real-time communication and portfolio optimization by routing information efficiently through a unified channel, reducing the communication overhead that would result from direct peer-to-peer interactions between multiple asset systems.
Data Source
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AI summary
Disclosed is a system, apparatus and method (400) for managing plurality of as-sets (105, 302A-N) in technical installation. The method (400) comprising receiving, by a processing unit (135), a set of requirements for managing the plurality of assets (105, 302A-N), selecting one or more assets (105, 302A-N) from the as-sets (105, 302A-N) based on the received set of requirements, mapping the one or more assets (105, 302A-N) to corresponding sensing units (125), extracting information associated with the selected one or more assets (105, 302A-N) and the received set of requirements, determining performance indicator based on information extracted from the knowledge base (122) and mapped sensing units (125), defining workflow to be executed based on the determined at least one performance indicator, selecting configured machine learning model from a set of ma-chine learning models based on the defined workflow, and determining an out-come of the selected machine learning model based on the received set of requirements.