Asset Performance Assurance Using Fault-Based Risk Comparison
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Solution Overview
Problem
Traditional data analytics and digital transformation for asset management are inefficient, requiring human intervention and manual configuration, making it difficult to identify and address issues in large portfolios of assets, and often result in suboptimal use of computing resources.
Innovation Solution
A system and method for real-time asset analytics that includes a processor-based platform for generating performance assurance insights by determining risk levels based on fault descriptors and asset data, using predetermined relationships between faults and performance indicator thresholds, to prioritize and address issues dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analytics and digital transformation are used for asset management, then human expertise can be applied to analyze assets, but it becomes difficult to identify and fix issues in large portfolios of assets and computing resources are used inefficiently
Solution Approach 1:
The system enables automated self-service through intelligent agents that independently analyze asset data, identify issues, and generate insights without human intervention. The agents autonomously process large portfolios of assets, determining risk levels and performance deviations automatically, thereby resolving the contradiction between maintaining analysis accuracy and improving management efficiency.
Solution Approach 2:
Manual mechanical analysis by human experts is replaced with automated computational systems using machine learning models and intelligent agents. The system substitutes human cognitive processes with algorithmic analysis that can process vast amounts of asset data rapidly, achieving both high precision in issue identification and improved productivity through automation.
2Loss of information
If limited time is spent on modeling asset data, then computational resources are conserved, but insufficient insights are generated from the data
Solution Approach 1:
The system performs preliminary actions by pre-configuring intelligent agents with predefined analytical capabilities and models before data analysis begins. These agents are prepared in advance to immediately process asset data and generate insights, eliminating the need for time-consuming manual modeling while ensuring comprehensive data analysis and high-quality insights are produced.
3Ease of operation
If traditional data analytics approaches are used, then implementation is straightforward, but computing resources are not optimized and response time to asset issues is delayed
Solution Approach 1:
The system transitions from static traditional analytics to dynamic intelligent agents that continuously monitor asset data and adapt their analysis in real-time. The agents dynamically adjust their computational resources based on detected anomalies and risk levels, enabling faster response to asset issues while maintaining ease of operation through automated decision-making processes.
Data Source
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.