Multivariate Optical Computing System Data Validation

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Solution Overview

Problem

Existing multivariate optical computing systems face challenges in accurately measuring light intensity due to interference from multiple factors, leading to inaccurate data analysis, especially in industrial applications where precision and real-time analysis are crucial.

Innovation Solution

A method involving a multivariate optical computing system that receives and processes light signals interacted with materials using spectral elements and detectors, performing dot product operations with weighted regression vectors to classify and validate measurement results, enabling accurate and efficient data analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If multiple wavelength bands are measured to capture comprehensive light information, then the information content increases, but measurement precision deteriorates due to interfering data from multiple factors

Engineering Contradiction:

Improveinformation contentVSAvoidmeasurement precision

Core Design Contradiction:

Loss of informationVSMeasurement precision

Solution Approach 1:

The patent segments the comprehensive light information into multiple wavelength bands, measuring each band separately. This allows the system to capture comprehensive information while managing interference by processing each segment (wavelength band) individually through regression analysis, rather than being overwhelmed by the combined interference of all bands simultaneously.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameter being measured from raw light intensity to regression coefficients that represent specific material properties. By transforming the measurement parameter through multiple linear regression analysis, the system extracts meaningful information about material composition while filtering out interfering data from other factors.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If conventional measurement methods are used to obtain reference data, then data accuracy improves, but device complexity and analysis time increase

Engineering Contradiction:

Improvedata accuracyVSAvoidinstrumentation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical measurement systems with an optical computing system that performs multiple linear regression analysis. Instead of using sophisticated physical apparatus to isolate and measure specific material properties directly, the system uses mathematical computation on optical signals to achieve the same measurement goal with simpler instrumentation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs preliminary regression analysis during the calibration phase to establish the relationship between wavelength band intensities and material properties. This preliminary computation creates a model that can then be applied to subsequent measurements without requiring complex real-time analysis, simplifying the measurement process while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If principal component analysis is used to compress data, then measurement precision improves by isolating relevant signals, but device complexity increases due to additional processing requirements

Engineering Contradiction:

Improvesignal isolation accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms the spectral data from the original wavelength domain to a new domain defined by principal components through singular value decomposition. This parameter transformation compresses the data while preserving the most significant variations, isolating relevant signals from noise and interference. The transformed parameters (principal component scores) provide a more compact and informative representation of the original data.

Inventive Principle:

Principle #35Parameter changes

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enhances measurement precision and simplifies instrumentation, allowing for real-time analysis of materials by isolating relevant spectral ranges and reducing noise, thereby improving the accuracy of chemical and physical property measurements.

Implementation Method 1

receiving a first signal from a first detector indicative of light that has interacted with a material of interest and a multivariate optical element

Methodology Applied

Scientific EffectPhotoelectric Effect: Photoelectric Effect

Data Source

PatentUS9170154B2Data validation and classification in optical analysis systems

Publication Date: 2015.10.27 HALLIBURTON ENERGY SERVICES INC

AI summary

A method of classifying information in an optical analysis system includes obtaining calibration data defining a plurality of data points, each data point representing values for two or more detectors when sampling a material used to construct a multivariate optical element. Based on the calibration data, one or more validation models can be developed to indicate one or more ranges of expected results. Validation data comprising the models can be used to compare data points representing values for two or more detectors when performing a measurement of a material to determine if the data points fall within an expected range. Classification data can be generated based on the comparison and, in some embodiments, one or more indicators, such as a confidence level in a measurement, can be provided.