Optical Emission Spectra Correction for Temperature-Driven Drift
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Solution Overview
Problem
Optical emission spectra are sensitive to changes in operating conditions, particularly temperature, leading to spectral distortions that conventional methods struggle to correct effectively, requiring the spectrometer to operate in a constrained stable range and wasting time and resources.
Innovation Solution
A machine learning model is trained to predict transform parameters based on operating conditions, applying transformations such as translations, rotations, and local distortions to mitigate spectral distortions, allowing the spectrometer to operate over a wider range of conditions without the need for peak identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spectral correction methods are used, then spectral accuracy is maintained under stable conditions, but the spectrometer requires operation in a constrained stable temperature range, reducing adaptability and increasing loss of time
Solution Approach 1:
The patent transforms the spectral correction problem from a geometric transformation task to a parameter prediction task. A machine learning model is trained to predict spectral parameters (peak positions, intensities, shapes) directly from operating condition parameters (temperature, humidity, pressure). This allows the system to adapt to varying operating conditions by predicting and correcting spectral parameter deviations, thereby maintaining measurement precision while expanding the acceptable operating condition range.
2Measurement precision
If conventional peak identification methods are used for spectral registration, then spectral distortion correction is achieved, but the process requires time-consuming peak identification and stabilization periods, reducing productivity
Solution Approach 1:
The patent replaces the traditional mechanical/optical spectral registration process with a data-driven machine learning approach. Instead of using peak identification algorithms and geometric transformations, the system uses a trained model that directly predicts corrected spectral parameters from operating conditions. This substitution eliminates the need for time-consuming peak identification and stabilization periods, significantly improving throughput while maintaining correction accuracy.
Solution Approach 2:
The patent implements preliminary action by training the machine learning model in advance using spectral data collected under various operating conditions. The model learns the relationships between operating parameters and spectral variations beforehand, enabling rapid prediction and correction during actual measurements without requiring real-time peak identification or stabilization periods.
3Measurement precision
If the spectrometer operates in a constrained stable temperature range, then spectral measurements are accurate, but instrument availability and resource utilization are reduced
Solution Approach 1:
The patent implements a feedback mechanism where operating condition parameters are continuously monitored and fed into the machine learning model. The model predicts spectral parameter deviations based on current operating conditions and provides correction values. This closed-loop feedback system enables the spectrometer to maintain measurement accuracy across a wider range of operating conditions, thereby improving instrument availability without sacrificing precision.
Data Source
AI summary
Disclosed herein are scientific instrument support systems, related methods, computing devices and computer-readable media. A method of mitigating distortion of an optical emission spectrum obtained from an optical emission spectrometer is provided. The method may comprise a step of obtaining a spectrum recorded with the spectrometer and a respective one or more condition parameters indicative of an operating condition at a time of recording the spectrum. The method may further comprise a step of providing a model configured to output, in response to the one or more condition parameters, one or more transform parameters of a transformation to be applied to the obtained spectrum. A transformation may be applied in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition.