Fireset RLC Model Derivation via Waveform Error Minimization
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Solution Overview
Problem
Conventional methods for qualifying new fireset designs are time-consuming and prone to human error, as they require iterative trial and error processes to determine RLC characteristics, leading to inconsistent results due to subjective selection of waveform points and manual data alterations.
Innovation Solution
A computer-implemented method that acquires and processes waveform data to derive RLC values by time-shifting and scaling the waveform, generating model data through iterative loops, and determining percentage error values to select an ideal waveform for accurate RLC value derivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software programs are used to analyze fireset electrical properties, then the analysis can be performed, but the results are inconsistent due to subjective selection of waveform points and manual data alterations
Solution Approach 1:
The system automatically selects critical waveform points and performs data processing without requiring manual user intervention. The computer systematically identifies maximums, minimums, zero crossings, and slopes, eliminating subjective human judgment and ensuring consistent, repeatable results across different users and analyses.
Solution Approach 2:
The patent replaces manual mechanical processes (user selection of points, manual data alteration) with automated computational algorithms. The system uses programmed logic to identify waveform characteristics and perform calculations, substituting human operator actions with deterministic software processes that yield consistent results.
2Measurement precision
If iterative trial and error process is used to determine best-fit waveform, then the analysis can be performed, but the process is time-consuming and requires multiple iterations
Solution Approach 1:
The system performs preliminary automated selection of critical waveform points and preliminary calculation of RLC characteristics in a single pass through the data. By pre-identifying all necessary waveform features algorithmically, the system eliminates the need for repeated trial and error iterations, achieving accurate results in one execution.
Solution Approach 2:
The system incorporates feedback mechanisms where the computer continuously refines waveform analysis by comparing calculated RLC values against the original waveform data, automatically adjusting parameters to achieve the best fit without requiring manual intervention or multiple external iterations.
3Ease of operation
If user directly alters input file to analyze data, then the analysis can be performed, but human errors are introduced in signal inversion, conversion, and scaling
Solution Approach 1:
The system automatically performs all necessary data processing operations including signal inversion, unit conversion from voltage to amperes, and scaling to account for instrument attenuation. The computer executes these transformations programmatically without requiring manual user actions, thereby eliminating human error while maintaining analytical flexibility.
4Productivity
If limited number of iterations are used to determine least error match, then the calculation is faster, but the extracted circuit values are not the best fit for the waveform
Solution Approach 1:
The system uses feedback loops where the computer calculates RLC values, compares the resulting waveform against the original data, and automatically iterates to refine the solution. This continuous feedback process ensures that the final extracted circuit values represent the true best-fit solution without requiring pre-defined limits on the number of iterations.
Data Source
AI summary
A computer-implemented method for deriving resistance, inductance, and capacitance (RLC) values of an RLC equivalent circuit model associated with a fireset. The method may comprise steps of: acquiring and storing waveform data of a current discharge pulse capable of executing a fireset; plotting a waveform based on the waveform data; adjusting the plotted waveform by time-shifting and scaling the plotted waveform based on user input values; determining a time offset measurement and initial frequency measurement from the time-shifting and scaling steps, respectively; generating a plurality of model data based on the time offset measurement, initial frequency measurement, and user input values; determining a plurality of percentage error values corresponding to the model data; determining an ideal waveform by selecting a least of the percentage error values and associated model data; deriving ideal RLC values from the ideal waveform and associated model data; and displaying the ideal waveform and plotted waveform.