Fuse Monitoring Assembly for Remaining Service Life Prediction
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Solution Overview
Problem
Existing fuse monitoring systems for high voltage electrical power systems in electric vehicles are too complex, expensive, and large, failing to provide effective monitoring and prediction of fuse service life under severe operating conditions.
Innovation Solution
A compact, affordable, and reliable fuse monitoring system that uses sensors to measure real-time environmental and electrical performance parameters, applying a model to predict the remaining service life of the fuse and provide alerts for proactive replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex monitoring systems are used to track fuse service life, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The monitoring system is segmented into independent functional modules: environmental sensors (temperature, humidity, vibration), electrical sensors (current, voltage), a processing unit for data analysis, and a communication module. Each module performs a specific function, allowing the system to achieve high measurement precision through specialized sensors while managing complexity through modular architecture.
Solution Approach 2:
The processing unit performs multiple functions: it processes data from various sensor types, applies predictive algorithms for service life estimation, generates alerts, and communicates results. This multi-functional approach consolidates what could be separate complex systems into a single integrated unit, improving measurement precision without proportionally increasing device complexity.
2Reliability
If comprehensive environmental and electrical parameters are monitored, then reliability of service life prediction improves, but device complexity and manufacturing cost increase
Solution Approach 1:
The system incorporates sensors and processing capabilities from the outset to continuously collect environmental and electrical parameters. By performing preliminary monitoring and analysis, the system builds a comprehensive data profile that reliably predicts service life without requiring complex post-installation configurations or manual setup procedures, thus maintaining ease of manufacture while improving reliability.
Solution Approach 2:
The processing unit automatically analyzes incoming sensor data, applies predictive algorithms, and generates service life estimates without external intervention. The system self-calibrates and adapts to different fuse types through automated parameter recognition, reducing manufacturing complexity by eliminating the need for manual configuration while enhancing prediction reliability through consistent automated analysis.
3Loss of time
If real-time monitoring of multiple parameters is implemented, then loss of time for detecting fuse fatigue is reduced, but device complexity increases
Solution Approach 1:
The monitoring system operates continuously, with sensors constantly measuring environmental and electrical parameters and the processing unit continuously analyzing data for signs of fuse fatigue. This uninterrupted monitoring eliminates detection delays, allowing immediate identification of degradation trends while using efficient algorithms that process data in real-time without requiring complex batch processing or periodic sampling systems.
Solution Approach 2:
The system implements continuous feedback loops where sensor measurements are immediately processed, and results are used to adjust monitoring parameters and generate real-time service life predictions. This feedback mechanism enables rapid detection of fuse fatigue by comparing current measurements against historical data and predictive models, reducing detection time while using straightforward feedback control rather than complex adaptive systems.
Data Source
AI summary
A fuse monitoring assembly for monitoring one or more fuses within a fuse housing includes an upper housing and a lower housing coupled to the upper housing and defining a housing cavity; at least one sensor configured to measure fuse data associated with the one or more fuses, the fuse data including operational data of the fuse and environmental data in which the one or more fuses are located; and at least one processor communicatively coupled to the at least one sensor to transmit the fuse data to a remote computing device. The remote computing device is configured to receive the fuse data; determine an estimated remaining fuse service life by analyzing the fuse data using a combination of a physics model and a machine-learning model; and generate a fuse message based on the analysis. The sensor and the processor are positioned within the housing cavity.