Power Asset Health Scoring With ESA Threshold Adjustment
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Solution Overview
Problem
Existing power system asset management is time-consuming and costly, with manual processes failing to efficiently assess and prioritize the health of large fleets of assets, leading to unnecessary maintenance and operational inefficiencies.
Innovation Solution
An integrated condition monitoring system using Electrical Signature Analysis (ESA) for automated, online fleet management, which classifies assets into sub-systems, computes health indices and scores, and triggers alarms based on adjustable thresholds, leveraging intelligent electronic devices (IEDs) for data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual asset assessment processes are used, then comprehensive health information can be gathered, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical assessment processes with automated Electrical Signature Analysis (ESA) and online monitoring systems. Intelligent electronic devices (IEDs) automatically collect, analyze, and interpret asset health data, eliminating the need for time-consuming manual inspections while maintaining or improving assessment accuracy through consistent, data-driven evaluations.
Solution Approach 2:
The system enables assets to self-monitor and self-report their health status through integrated sensors and IEDs that continuously collect operational data. The automated analysis and ranking processes allow the fleet management system to self-assess asset conditions without external manual intervention, significantly reducing assessment time while maintaining comprehensive health monitoring.
2Loss of information
If comprehensive fleet monitoring is implemented, then asset health information is improved, but system complexity increases
Solution Approach 1:
The patent segments the fleet monitoring system into modular components: IEDs at the asset level for data collection, health index calculation modules for analysis, and ranking algorithms for prioritization. This segmentation allows comprehensive monitoring of multiple assets while managing complexity through standardized, interchangeable modules that can be independently configured and maintained.
Solution Approach 2:
The system employs universal IEDs and standardized health index calculations that can be applied across diverse asset types (motors, pumps, fans, etc.). The same core monitoring and analysis framework serves multiple functions including data collection, health assessment, ranking, and alert generation, reducing overall system complexity through multi-functional components.
3Productivity
If automated health scoring is implemented, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where health index calculations are continuously refined based on historical data, actual asset performance, and maintenance outcomes. The automated scoring system learns from patterns in the data and adjusts health index weights and thresholds, improving measurement precision over time while maintaining high productivity through automated processing of large asset fleets.
Data Source
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AI summary
Systems and methods are disclosed for motor health assessment and fleet management. An example method may include classifying a first power system asset into a first sub-system and a second sub-system. The example method may also include measuring, by a processor of a protection relay and from a first power system asset, electrical, thermal, and/or mechanical data associated with the first power system asset. The example method may also include identifying a first fault feature for the first sub-system, wherein the first fault feature is influenced by load oscillations in the first power system asset. The example method may also include comparing the first fault feature to a second fault feature of a third sub-system in a second power system asset, wherein the second fault feature is the same as the first fault feature, and wherein the second fault feature is not associated with load oscillations. The example method may also include adjusting a threshold value based on the comparison of the first fault feature to the second fault feature of the third sub-system. The example method may also include calculating, by the processor and for a first sub-system of the first power system asset, a first value based on the electrical, thermal, and/or mechanical data. The example method may also include calculating, by the processor, based on the irst value, and using recent measurement data, a second value associated with the first subsystem. The example method may also include calculating, by the processor and using historical average data, a third value associated with the first sub-system. The example method may also include determining, by the processor and based on the second value and the third value, a fourth value associated with the first power system asset. The example method may also include determining, by the processor, that the fourth value is greater than a threshold value. The example method may also include generating, by the processor, a warning based on the determination that the fourth value is greater than the threshold value.