Hybrid Generator Engine Screening Using Battery Load Steps
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Solution Overview
Problem
There is a critical need for efficient and reliable monitoring and evaluation of hybrid power systems to maintain components at high levels of efficiency and effectiveness, particularly in military applications where equipment performance and fuel efficiency are crucial, especially in contested environments.
Innovation Solution
The implementation of an intelligent diagnostic screening (IDS) system that includes a hybrid power system with an energy storage module and generator, equipped with sensors and a computer interface, which performs load tests to assess engine performance, calculates scores, and stores historical data for predictive maintenance and sequencing optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring and evaluation systems are implemented to assess engine health and predict maintenance needs, then system reliability and maintenance accuracy are improved, but device complexity and implementation cost increase
Solution Approach 1:
The diagnostic system is divided into separate functional modules including sensors for data collection, a processor for analysis, and interfaces for output. This modular segmentation allows the complex monitoring function to be implemented through manageable components that can be independently optimized and maintained.
Solution Approach 2:
The system introduces an intermediary diagnostic platform that mediates between the engine components and the maintenance decision-making process. This intermediary layer collects data from various sensors, processes information, and provides recommendations, simplifying the overall system architecture while improving reliability.
2Productivity
If frequent diagnostic testing and performance evaluation are conducted to maintain high levels of efficiency, then equipment effectiveness is improved, but loss of time and operational disruption increase
Solution Approach 1:
The system performs preliminary diagnostic actions by continuously monitoring engine parameters and identifying potential issues before they manifest as failures. This allows maintenance to be scheduled proactively during off-peak hours or planned downtime, minimizing operational disruption while maintaining high equipment effectiveness.
Solution Approach 2:
The diagnostic system operates continuously in the background, monitoring engine health without interrupting operational tasks. This continuous monitoring allows the system to maintain awareness of engine condition while preserving productivity, as the diagnostic function does not require stops or interruptions to its operation.
3Measurement precision
If detailed performance data collection and historical data storage are implemented to enable predictive maintenance, then measurement precision and maintenance accuracy are improved, but loss of information processing and storage requirements increase
Solution Approach 1:
The system extracts and isolates only the critical diagnostic information needed for predictive maintenance from the vast amount of available sensor data. By filtering and extracting only relevant parameters such as engine performance metrics and anomaly indicators, the system reduces information processing load while maintaining high measurement precision for the essential diagnostic functions.
Data Source
AI summary
Systems and methods for evaluating an engine associated with a hybrid power system, including initiating a load step on the engine using a battery of the hybrid power system, measuring a plurality of parameters associated with a response of the engine to the load step using a plurality of sensors, comparing the measured parameters associated with the response of the engine to baseline performance data associated with the engine, and calculating an engine performance score for the engine based on the comparison of the measured parameters to the baseline performance data.