Sensor device and method for characterizing metal chips
US-2024310267-A1 · Sep 19, 2024 · US
US10871476B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10871476-B2 |
| Application number | US-202016816717-A |
| Country | US |
| Kind code | B2 |
| Filing date | Mar 12, 2020 |
| Priority date | Oct 26, 2012 |
| Publication date | Dec 22, 2020 |
| Grant date | Dec 22, 2020 |
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Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.
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What is claimed is: 1. A method for generating a failure prediction for an engine of an engine type, the method comprising: receiving filtered particles filtered from lubricating fluid of the engine; using X-ray spectroscopy, analyzing the filtered particles and producing raw data relating to the filtered particles, the raw data including chemical compositions of individual filtered particles; using one or more processors and the raw data: categorizing individual particles into categories to generate categorized data, at least one category being defined by a chemical composition zone within a three-chemical-element system; comparing the categorized data with historical data associated with the engine type; generating the failure prediction based on the comparison and predefined rules, the failure predication being indicative of one or both of the following: when the engine is expected to fail and a mechanism of failure of the engine; and generating an output indicative of the failure prediction. 2. The method of claim 1 , wherein the raw data includes sizes and morphologies of individual filtered particles. 3. The method of claim 1 , wherein the filtered particles include particles smaller than about 30 μm in diameter. 4. The method of claim 1 , wherein the filtered particles include particles having a size between 0.5 μm and 1600 μm in diameter. 5. The method of claim 1 , wherein at least one category is defined according to a particle source. 6. The method of claim 1 , wherein the comparison comprises at least one of a calculation of deviation of the categorized data from the historical data, and a calculation of variation of the categorized data from the historical data. 7. The method of claim 1 , wherein the comparison comprises at least one of a comparison of the categorized data in each category to the historical data, and a comparison of a composite of the categorized data to the historical data. 8. The method of claim 1 , wherein the failure prediction is indicative of premature wear of a component of the engine. 9. The method of claim 1 , wherein using X-ray spectroscopy includes using X-ray fluorescence. 10. The method of claim 1 , comprising filtering out the plurality of particles from a sample of the lubricating fluid wherein the sample has a volume of 25 ml or less. 11. The method of claim 1 , wherein the the plurality of particles includes at least 1000 particles. 12. The method of claim 1 , comprising generating the failure prediction based on a number of particles in one of the categories. 13. The method of claim 1 , comprising generating the failure prediction based on a number of particles in the chemical composition zone. 14. The method of claim 1 , wherein the filtered particles include non-metallic particles and the raw data includes data relating to the non-metallic particles.
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