Magnetic Bearing Temporal Modeling for Nonlinear Operating Conditions
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Solution Overview
Problem
Existing methods for producing digital models of magnetic bearings are limited by their reliance on linear digital solvers and small signal excitations, which fail to accurately capture the nonlinear behavior of magnetic bearings across all operating conditions.
Innovation Solution
A method for generating a temporal model of a magnetic bearing using reference data that includes learning and validation data sets, with transient temporal data sets of waveforms and random amplitudes, allowing for the injection of these data sets into a nonlinear model of the magnetic bearing to collect output data and create a more accurate model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear digital solvers with small signal excitations are used, then the model construction is simple and computationally efficient, but the model accuracy deteriorates because nonlinear behavior cannot be captured
Solution Approach 1:
The patent changes the fundamental parameters of the identification method by transitioning from frequency domain analysis to temporal domain analysis, and from small signal excitations to transient signals with random amplitudes. This allows the model to capture nonlinear behaviors while maintaining computational efficiency through modern processing techniques.
Solution Approach 2:
The patent replaces traditional mechanical identification methods (frequency domain analysis with linear solvers) with a computational approach using temporal domain analysis and automatic learning algorithms. This substitution enables accurate capture of nonlinear phenomena without the limitations of linear approximation methods.
2Measurement precision
If piecewise linear approximation is used to account for nonlinearity, then some nonlinear phenomena are captured, but the model remains inaccurate because small signal excitations cannot represent all operating conditions
Solution Approach 1:
The patent introduces dynamic characteristics by using transient signals with random amplitudes instead of static or small sinusoidal excitations. This allows the system to be excited across its full operating range, capturing nonlinear behaviors under varying conditions that piecewise linear methods cannot represent.
Solution Approach 2:
The patent employs periodic transient signals that repeat the identification process with varied random amplitudes. This periodic action with varying parameters ensures comprehensive coverage of operating conditions while maintaining systematic data collection for model training.
3Reliability
If frequency domain identification is used, then the identification process is well-established, but the model cannot accurately represent transient and nonlinear operations
Solution Approach 1:
The patent inverts the traditional approach by moving from frequency domain to temporal domain analysis. Instead of transforming signals to frequency domain for identification, the method performs identification directly in the temporal domain, thereby preserving transient information that is lost in frequency domain transformations.
Data Source
AI summary
The method for obtaining a temporal model of a magnetic bearing provides the generation of reference data representative of characteristics of the bearing. The method includes the production of the temporal model of the magnetic bearing from the reference data having temporal data.