Virtual Sensor Modeling for Multi-Point Electric Motor Thermal Sensing
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Solution Overview
Problem
Existing technologies face challenges in accurately determining physical and control parameters of complex systems like electric motors, particularly in real-time, due to spatial distribution of parameters and limitations in sensor placement, leading to inaccuracies in thermal management and control.
Innovation Solution
A device utilizing trained machine learning units, combining different machine learning models such as AdaBoostRegressor and BayesianRidge, to determine parameters like temperature, state of health, and coolant flow rate at multiple target points within an electric motor, allowing for precise and efficient thermal management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensors are used to measure parameters at different spatial locations, then measurement accuracy may be improved, but device complexity and space requirements increase
Solution Approach 1:
The patent creates virtual copies of physical sensors through machine learning models. Instead of deploying physical sensors at every measurement point, the system trains ML models to replicate sensor behavior and predict parameters at multiple spatial locations. This virtual sensing approach maintains measurement accuracy while eliminating the need for complex physical sensor networks.
Solution Approach 2:
The patent replaces the mechanical/physical sensor system with an information-processing system based on machine learning. The physical measurement system is substituted by computational models that process input data and generate parameter predictions, thereby reducing device complexity and space requirements while maintaining measurement capabilities.
2Measurement precision
If physical sensors are installed at all necessary positions, then parameter determination accuracy improves, but space restrictions and installation difficulty worsen
Solution Approach 1:
The patent develops universal machine learning models that can determine multiple different parameters (temperature, pressure, flow rate, etc.) at multiple spatial locations using a single integrated system. This multi-functional approach eliminates the need for installing different physical sensors at different positions, greatly simplifying installation while maintaining comprehensive parameter determination capability.
3Measurement precision
If physical sensors are used to measure spatially distributed parameters, then gradient detection improves, but the number of sensors and system complexity increase
Solution Approach 1:
The patent uses machine learning models to create virtual sensors that replicate the measurement capabilities of physical sensors. By training these models on data from fewer physical sensors, the system can predict parameters and detect gradients at multiple locations without proportionally increasing the number of physical sensors required.
Solution Approach 2:
The patent combines multiple measurement functions and spatial locations into a unified machine learning model. Instead of using separate physical sensors for each measurement point, the system merges the measurement tasks into a single computational framework that processes input data and generates predictions for multiple parameters and locations simultaneously.
4Measurement precision
If complex analytical models are used to determine parameters, then determination accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent transforms the approach from using complex analytical models with many parameters to using machine learning models that learn parameter relationships from data. By changing from physics-based analytical models to data-driven ML models, the system achieves high determination accuracy while reducing computational complexity and processing requirements.
Data Source
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AI summary
A device 1 for determining values of parameters related to a machine, the device 1 comprising an input interface for receiving input data; one or more sets 2 of determination units 3, wherein each set 2 includes a plurality of determination units 3 and each determination unit 3 within one set 2 is configured to determine a value of a predetermined parameter for a target point based on the received input data, and wherein one or more determination units 3 from one set 2 are combined into a subset 5 of determination units 3 and each subset 5 is configured to determine a value of the predetermined parameter for a target point associated to the subset 5; and an output interface for outputting a value determined by the subset 5.