Power Asset Failure Prediction Using Similar-Asset Life Modeling
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Solution Overview
Problem
Conventional methods for predicting the failure of electrical power system assets are inefficient, often leading to unplanned outages and increased costs due to unpredictable asset failures, as they either require time-consuming manual inspections or scheduled replacements that may not align with actual asset health.
Innovation Solution
A method and system for predicting time-to-failure and remaining-life of power system assets by analyzing asset characteristics and associating them with a database of similar assets, calculating probability factors, and using machine-learning to enhance analysis confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection and testing of individual assets is performed periodically, then asset failure detection capability is improved, but time consumption and operational disruption increase
Solution Approach 1:
The patent replaces manual mechanical inspection methods with automated electronic monitoring systems and data analytics. Sensors continuously collect asset performance data, and algorithms automatically analyze this data to predict failures, eliminating the need for time-consuming manual inspections while maintaining or improving detection capability.
Solution Approach 2:
The patent introduces an intermediary analytical system that sits between the assets and human operators. This system processes raw asset data, applies predictive algorithms, and generates failure predictions, thereby automating the detection process and reducing the time required for manual assessment.
2Ease of manufacture
If assets are replaced according to a predetermined schedule, then maintenance planning is simplified, but resources are wasted on premature replacement or failures occur between replacements
Solution Approach 1:
The patent transitions from static predetermined replacement schedules to dynamic, condition-based replacement timing. The system continuously monitors asset health indicators and adjusts the replacement schedule in real-time based on actual asset condition, allowing replacements to occur optimally just before predicted failures without wasting resources on premature replacement.
Solution Approach 2:
The patent performs preliminary failure prediction through continuous monitoring and analytics before actual failures occur. This allows maintenance planning to be based on predicted failure timelines rather than fixed schedules, enabling resources to be allocated efficiently and replacements to be timed optimally.
3Reliability
If repairs are made after power outages occur, then service restoration is achieved, but additional costs and operational difficulties increase
Solution Approach 1:
The patent applies preliminary anti-action by predicting asset failures before they cause power outages. The system identifies deteriorating assets and schedules preventive repairs during planned maintenance windows, thereby preventing the harmful effect of unplanned outages and associated economic losses before they can occur.
Data Source
AI summary
A method for predicting time-to-failure and remaining-life of an electrical power system asset includes analyzing characteristics of the power system asset over a specified time interval; based on the analysis, associating the power system asset with a pool of similar power system assets having similar historical performance characteristics; and based on the association, calculating time-to-failure and remaining-life probability factors for the power system asset. A computer program product is also provided for carrying out the method, and the method may further include a mechanism whereby the computer program learns ways to modify and enhance the analysis of the performance characteristics to provide for higher confidence levels in time-to-failure and remaining-life probability calculations.