Method and system for failure prediction using lubricating fluid analysis
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.
1. A method for generating a failure prediction for an engine of an engine type, the method comprising:
receiving a sample of lubricant fluid collected from the engine, the sample of lubricant fluid having particles suspended therein;
filtering out a plurality of the particles from the sample of lubricant fluid;
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 a 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 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 the failure prediction is indicative of excessive vibration.
10. The method of claim 1 , wherein the failure prediction is indicative of contamination following engine maintenance.
11. The method of claim 1 , wherein the failure prediction is indicative of gear degradation.
12. The method of claim 1 , wherein the failure prediction is indicative of bearing degradation.
13. The method of claim 1 , wherein using the X-ray spectroscopy includes using X-ray fluorescence.
14. The method of claim 1 , wherein the sample of lubricant has a volume of 25 ml or less.
15. The method of claim 1 , wherein the sample of lubricant contains at least 1000 particles.
16. The method of claim 1 , comprising analyzing each of the filtered particles and producing raw data relating to each of the filtered particles.
17. The method of claim 1 , comprising generating the failure prediction based on a number of particles in one of the categories.
18. 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.