Fluid Origin Identification via Spectral Analysis
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Solution Overview
Problem
Existing methods for monitoring oil condition and quality do not effectively determine the origin of a fluid used in machines, leading to potential premature wear or damage due to improper fluid usage, which can result in unnecessary maintenance costs.
Innovation Solution
A system and method utilizing spectral data from a spectral measurement device, processed by a machine learning engine to determine the origin of a fluid, including its manufacturer, brand, and geographic source, based on transmittance or emission spectra, and outputting this information for identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral measurement and machine learning analysis are implemented to determine fluid origin, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent introduces spectral data as an intermediary that captures fluid characteristics without requiring direct physical or chemical interaction with the fluid. The spectral measurement device obtains transmittance or emission spectra, which serve as a mediator between the fluid sample and the analysis system, enabling origin determination through pattern recognition rather than complex chemical analysis
Solution Approach 2:
The patent replaces complex mechanical or chemical fluid analysis systems with an optical-based spectral measurement system combined with machine learning. Instead of using sophisticated laboratory equipment for fluid characterization, the system uses spectral transmittance or emission measurements processed through analytical models to identify fluid origin, substituting physical/chemical methods with optical and computational approaches
2Loss of information
If comprehensive spectral analysis is performed to identify fluid origin, then information completeness improves, but loss of time increases
Solution Approach 1:
The patent pre-trains machine learning models using extensive spectral data from fluids of known origins before actual fluid identification is needed. The analytical models are developed offline through supervised learning with labeled spectral datasets, so that when actual fluid samples are analyzed, the pre-trained models can rapidly classify fluid origin without requiring time-consuming real-time computation or extensive real-time data processing
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
Enables accurate identification of the fluid's origin, preventing premature wear and damage by ensuring the correct fluid is used in machines, thereby reducing maintenance and repair expenses.
Implementation Method 1
a spectral measurement device configured to generate spectral data for a fluid
Data Source
AI summary
A system for determining an origin of a fluid from a machine may include a spectral measurement device configured to generate spectral data for a fluid, and a transmitter in communication with the spectral measurement device and configured to transmit the spectral data. The system may also include a receiver in communication with the transmitter and configured to receive a transmission indicative of the spectral data from the transmitter. The system may further include a processor in communication with the receiver and configured to cause execution of an analytical model configured to determine, based at least in part on the spectral data, fluidic information related to the fluid, the fluidic information including an indication of an origin of the fluid. The system may also include an output device in communication with the processor and configured to output the indication of the origin of the fluid.