Asset Performance Assurance Modeling for Fault Risk Prioritization
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Solution Overview
Problem
Traditional data analytics and digital transformation for asset management involve human interaction, making it difficult to efficiently identify and address issues in large portfolios of assets, such as 1000 buildings with 100 assets each, due to limited time spent on data modeling and inefficient use of computing resources.
Innovation Solution
A system comprising processors and memory with programs that receive requests for performance assurance insights, determine risk levels based on fault descriptors and asset data, and generate insights by comparing these risk levels, employing real-time models and visual analytics to provide actionable recommendations for sustained peak performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human interaction is used for data analytics and digital transformation of asset data, then insights can be generated, but the process becomes inefficient and time-consuming when managing large portfolios of assets
Solution Approach 1:
The system enables automated self-service through AI/ML models that automatically analyze asset data, identify issues, and generate insights without requiring manual human intervention for each asset, thereby improving productivity while reducing time investment
Solution Approach 2:
Manual human analysis and data modeling processes are replaced with automated computational systems using machine learning algorithms and digital twins, substituting mechanical human effort with automated electronic processing to eliminate time constraints
2Use of energy by moving object
If computing resources are traditionally allocated for data analytics, then basic processing can be performed, but resource utilization remains inefficient for large-scale asset portfolios
Solution Approach 1:
The system segments computing resources and asset portfolios into manageable units, allowing parallel processing and distributed computation across multiple systems, which optimizes resource utilization while maintaining high productivity for large-scale analytics
Solution Approach 2:
The computing platform is designed with multi-functional capabilities to handle diverse asset types and analytics workloads using the same infrastructure, maximizing resource utilization efficiency while supporting scalable productivity improvements
Data Source
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AI summary
Various embodiments described herein relate to performance assurance modeling for a portfolio of assets. In this regard, a request to generate one or more performance assurance insights related to one or more assets is received. The request includes a fault descriptor describing one or more faults associated with the one or more assets. In response to the request, a first risk level associated with the one or more faults is determined based on the fault descriptor and asset data associated with the one or more assets. Additionally, in response to the request, a second risk level associated with the one or more faults is generated based on one or more predetermined relationships between faults and asset performance indicator thresholds. The one or more performance assurance insights are then generated based on a comparison between the first risk level and the second risk level.