Fuse Life Monitoring Using Sensor Data and ML Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fuse monitoring systems for high voltage electrical power systems in electric vehicles are complex, expensive, and large, failing to effectively monitor environmental and operational parameters to predict fuse service life, leading to premature failure and unplanned downtime.
Innovation Solution
A computationally-efficient and cost-effective fuse monitoring system that measures real-time parameters such as temperature, humidity, vibrations, and current, using sensors and machine learning models to assess the remaining service life of fuses, providing alerts and recommendations for maintenance and inventory management.
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 patent extracts only the essential parameters needed for fuse service life prediction (temperature, humidity, current, voltage) from the complex environmental and operational conditions, focusing monitoring efforts on the most critical factors that actually affect fuse degradation rather than attempting to measure all possible variables
Solution Approach 2:
The system transforms raw sensor data into meaningful service life predictions by changing parameters through machine learning model processing, converting multiple physical measurements into a single predictive metric that indicates remaining fuse life without requiring complex real-time analysis of all individual parameters
2Reliability
If comprehensive environmental and operational parameters are monitored, then reliability of service life prediction improves, but device complexity and cost increase
Solution Approach 1:
The monitoring system is designed to serve multiple functions: it tracks environmental conditions, monitors operational parameters, predicts service life, and provides maintenance alerts all through a single integrated system, making the complexity worthwhile by delivering comprehensive fuse management capabilities in one unified solution
Solution Approach 2:
The system performs preliminary monitoring and analysis of fuse conditions continuously in the background, building up a historical data profile that enables accurate service life prediction before actual fuse failure occurs, allowing proactive maintenance scheduling rather than reactive responses
3Productivity
If real-time monitoring of multiple parameters is implemented, then productivity through reduced downtime improves, but loss of energy and cost increase
Solution Approach 1:
The system monitors parameters continuously but only triggers full alerts and notifications when prediction confidence thresholds are met or when service life reaches critical levels, performing partial monitoring actions at full intensity only when necessary rather than maintaining maximum monitoring effort constantly
Solution Approach 2:
The machine learning model provides continuous feedback on prediction confidence levels and fuse health status, allowing the system to adjust its monitoring intensity and alert frequency dynamically, reducing energy consumption during stable periods while maintaining high surveillance when degradation is detected
Data Source
AI summary
A fuse monitoring system for monitoring a fuse is provided. The system includes a fuse monitoring assembly and a fuse monitoring computing device. The fuse monitoring assembly includes at least one sensor configured to measure fuse data associated with the fuse, the fuse data including operational data of the fuse and environmental data of an environment in which the fuse locates, the environmental data including shock and vibrations. The fuse monitoring assembly also includes at least one processor configured to transmit the fuse data to a remote computing device. The fuse monitoring computing device is positioned remotely from the fuse monitoring assembly, the fuse monitoring computing device including at least one processor in communication with at least one memory device. The fuse monitoring computing device is programmed to receive, from the fuse monitoring assembly, the fuse data, analyze the fuse data, and generate a fuse message based on the analysis.