Hybrid Generator Engine Diagnostics Using Load-Step Digital Twins
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Solution Overview
Problem
The challenge of maintaining the efficiency and effectiveness of hybrid power systems, particularly in military applications, is exacerbated by the need for precise monitoring and predictive maintenance of engine-driven components to avoid sudden failures and ensure reliable power supply in challenging environments.
Innovation Solution
An intelligent diagnostic screening (IDS) system that includes a hybrid power system with sensors, a computer, and a digital twin, which performs load tests on the engine to evaluate performance, calculate scores, and predict maintenance needs, optimizing generator sequencing for efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for hybrid power systems, then system complexity is reduced, but measurement precision and reliability of performance assessment deteriorate
Solution Approach 1:
The system creates a digital twin (virtual copy) of the physical hybrid power system that replicates its behavior and performance characteristics. This digital model allows for precise monitoring and assessment without adding physical complexity to the actual system, as the monitoring functions are performed in the virtual domain.
Solution Approach 2:
The digital twin acts as an intermediary between the physical system and the monitoring/analysis functions. Instead of directly instrumenting the physical system with numerous sensors and measurement devices, the digital twin serves as a mediator that receives data from the physical system and provides the basis for performance assessment.
2Reliability
If comprehensive diagnostic screening is implemented, then reliability of power supply improves, but loss of time for testing and evaluation increases
Solution Approach 1:
The system performs diagnostic screening and performance assessment in advance before critical failures occur. By continuously monitoring and evaluating system health through the digital twin, maintenance needs are identified proactively, allowing for planned interventions rather than reactive repairs that would cause downtime.
Solution Approach 2:
The diagnostic system operates continuously during normal system operation rather than requiring separate testing periods. The digital twin enables real-time performance assessment without interrupting the power supply function, maintaining continuous useful action of both the power system and the diagnostic system.
3Productivity
If predictive maintenance is implemented using digital twin technology, then productivity through optimized generator sequencing improves, but device complexity increases
Solution Approach 1:
The digital twin creates a virtual representation of the hybrid power system that can be used for simulation and optimization studies without affecting the physical system. Generator sequencing strategies can be developed and tested in the digital domain, then implemented in the physical system, improving productivity without adding physical complexity.
Solution Approach 2:
The system dynamically adjusts generator sequencing based on real-time performance data from the digital twin. The optimization is not static but adapts to changing system conditions, component health states, and operational requirements, enabling flexible productivity optimization without permanent structural changes.
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.