AI Asset Assessment for Private Electrical Equipment Diagnostics
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Solution Overview
Problem
Current electrical equipment asset condition diagnostics rely on industry standards and expert knowledge, but face challenges in sharing feedback and aggregating data from multiple owners, leading to limited information exchange and inefficient decision-making due to confidentiality and data privacy concerns.
Innovation Solution
An AI/ML driven assessment system that anonymously shares operational data and analysis across a community of users, providing actionable insights and recommended responses while maintaining confidentiality, and dynamically updates based on user feedback, using a centralized database that integrates data from various sources, including sensors, user manuals, and industry standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If operational data is shared among multiple users to improve diagnostic accuracy, then information completeness improves, but data privacy and confidentiality are compromised
Solution Approach 1:
The patent introduces an AI/ML-driven assessment system as an intermediary that processes and analyzes operational data from multiple electrical equipment assets. The system aggregates data from multiple sources to provide comprehensive diagnostic information while maintaining confidentiality by processing data through centralized AI algorithms rather than direct user-to-user sharing. This resolves the contradiction by enabling information completeness through aggregation while protecting data privacy through automated intermediary processing.
2Loss of information
If a forum is used to exchange equipment failure experiences, then knowledge sharing improves, but participation and information linking remain insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the AI/ML system continuously learns from user interactions, equipment responses, and diagnostic outcomes. The system processes operational data, generates assessments, and refines its algorithms based on feedback loops, enabling automatic linking of solutions to relevant users and equipment. This replaces manual forum participation with automated feedback-driven information exchange, improving both knowledge sharing and efficiency.
3Object-affected harmful factors
If each user aggregates data from their own assets only, then data privacy is maintained, but statistical significance is insufficient
Solution Approach 1:
The patent merges operational data from multiple electrical equipment assets owned by different users into a centralized database processed by AI/ML algorithms. The system combines datasets to achieve statistical significance for predictive analytics and failure pattern recognition while maintaining privacy through centralized processing rather than data disclosure. This enables the system to leverage collective data value without compromising individual user privacy, as the AI system analyzes aggregated patterns rather than exposing individual asset data.
Data Source
AI summary
An Artificial Intelligence/Machine Learning driven assessment system for monitoring electrical equipment assets includes a computer system that is configured to receive user-provided asset data associated with operation of a plurality of electrical equipment assets operated by a plurality of users/owners, where the identity of any asset in the database is restricted and only known to the user that owns/operates the asset. The computer system is configured to analyze the user data in conjunction with a pooled knowledge database so as to generate courses of action or assessments for the monitored electrical equipment assets and to update the analysis process based on feedback from a comparison of the assessment or course of action with an actual outcome.