Electric Motor Thermal Load Management via Predictive Restart Curves
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Solution Overview
Problem
Existing methods for determining the optimal restart time for electric motors exposed to thermal stress due to rotary movement are inefficient, often resulting in prolonged cooling times that are not necessary, leading to potential damage and inefficiencies.
Innovation Solution
A method that uses measurement data and mathematical models to predict temperature curves for different restart times, visualizing these predictions on a color scale to determine the quality of restart times and minimize thermal load, allowing for precise and efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative cooling times are used to prevent thermal damage, then reliability is improved, but productivity deteriorates due to extended idle times
Solution Approach 1:
The system dynamically changes the cooling time parameter based on actual measured temperature values and predicted temperature curves. Instead of using fixed conservative cooling times, the restart time is adjusted according to real-time thermal state assessment, allowing shorter cooling periods when temperatures permit while maintaining safety margins through continuous monitoring and prediction
Solution Approach 2:
The system implements feedback by continuously measuring temperature values inside the motor, comparing them against predicted temperature curves generated by mathematical models, and using this information to determine optimal restart times. This closed-loop approach replaces open-loop conservative timing with adaptive decision-making based on actual thermal conditions
2Measurement precision
If mathematical models with multiple solutions are used to calculate restart times, then measurement precision is improved, but device complexity increases making real-time calculation difficult
Solution Approach 1:
The system performs preliminary actions by pre-calculating multiple temperature curves representing different possible thermal scenarios before operation. These pre-computed curves serve as reference solutions that can be quickly matched against actual measured temperatures during operation, avoiding the need to solve complex mathematical models in real-time while maintaining prediction accuracy
Solution Approach 2:
The system uses simplified mathematical models that provide adequate prediction accuracy without the computational burden of complex physics-based simulations. These lighter models are computationally inexpensive and can be executed in real-time, sacrificing some theoretical precision for practical implementability and speed
3Manufacturing precision
If mathematical models disregard experience-based values, then manufacturing precision is improved through pure calculation, but reliability deteriorates due to suboptimal restart time determination
Solution Approach 1:
The system merges mathematical models with experience-based values by integrating empirically determined parameters (such as typical cooling rates, thermal thresholds, and operational patterns) into the mathematical framework. This combination allows the model to maintain calculation precision while incorporating practical wisdom that improves the accuracy of restart time predictions and overall system reliability
Data Source
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AI summary
The invention describes a method for the computer-assisted operation of an electric motor (M) which is exposed to a thermal load as a result of the rotational motion of its rotor (T) during operation. In this method, measured data is received during the operation of the electric motor (M). One or more temperature values are derived from the measured data. A number of temperature characteristics curves (TPi) are then forecast with differing restart times (t(TPi)) for defining a cooling period for reducing the thermal load on the electric motor (M), wherein the approximated temperature value, which results from the approximated temperature value is used as the specific starting value for a restart in the temperature characteristics curve (TPi) to be forecast. The temperature characteristics curve (TPi) lying in the past is visualised together with the number of forecast temperature characteristics curves (TPi) in a time-temperature graph, wherein the number of forecast temperature characteristics curves (TPi) represents a possible restart of the electric motor (M) at the particular restart time (t(TPi)) with the starting value determined in each case. A quality metric (GM) is determined for each of the number of forecast temperature characteristics curves (TPi). Finally, the quality metrics (GM) are mapped on a colour scale (20), which is visually output on a user interface, wherein, in response to a user interaction input via the user interface (13), time information for the restart of the electric motor (M) is output.