Method and system for failure prediction using lubricating fluid analysis
US-9897582-B2 · Feb 20, 2018 · US
US10151739B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10151739-B2 |
| Application number | US-201615137841-A |
| Country | US |
| Kind code | B2 |
| Filing date | Apr 25, 2016 |
| Priority date | Apr 25, 2016 |
| Publication date | Dec 11, 2018 |
| Grant date | Dec 11, 2018 |
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There is described herein methods and systems for comparing an engine fluid signature of a first engine with engine fluid signatures of other engines of a same engine family. A delta signature is obtained via the comparison and delta signatures below a given threshold are considered similar. Historical data for each engine having a similar signature may then be used to determine the condition and the future states of the first engine.
Opening claim text (preview).
The invention claimed is: 1. A method for evaluating a condition of an engine, the method comprising: obtaining a sample signature of a first engine, the sample signature characterizing the first engine as a function of a fluid sample of the first engine; comparing the sample signature to a plurality of reference signatures obtained from reference engines belonging to a common family with the first engine; selecting ones of the reference engines for which a difference between a corresponding reference signature and the sample signature is below a threshold; and providing historical engine data of the selected ones of the reference engines as a basis for evaluating the condition of the first engine; wherein the difference between the corresponding reference signature and the sample signature is found using: ∑ i = 1 n W Ti ( D Ei - D Ci ) 2 D Ei + D Ci where n is a number of data sets of the fluid sample, W Ti is a total weight assigned to a data set i, D Ei is a value of a data set i of the sample signature, and D Ci is a value of a data set i of the reference signature. 2. The method as claimed in claim 1 , wherein the first engine and the reference engines belong to a common family when they correspond to a same engine model. 3. The method of claim 1 , wherein providing historical engine data of the selected ones of the reference engines comprises providing a total number of operating hours of the selected ones of the reference engines. 4. The method of claim 1 , wherein providing historical engine data of the selected ones of the reference engines comprises identifying which portion of the selected ones of the reference engines ran without any problems for at least one total number of operating hours. 5. The method of claim 1 , further comprising estimating a condition of the first engine based on the historical data of the selected ones of the reference engines. 6. The method of claim 1 , wherein obtaining a sample signature of the first engine comprises obtaining fluid sample data and generating the sample signature. 7. The method of claim 6 , wherein generating the sample signature comprises assigning weights to different data sets of the fluid sample data, each one of the data sets corresponding to one or more classes of materials into which particles of the sample fluid are sorted. 8. The method of claim 7 , wherein assigning weights to different data sets comprises assigning a total weight to each data set, the total weight composed of a predetermined weight and a variable weight. 9. The method of claim 8 , wherein the variable weights are based on a deviation of a given value for a given data set from an average value for the given data set. 10. A system for evaluating a condition of an engine, the system comprising: a processing unit; and a non-transitory memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining a sample signature of a first engine, the sample signature characterizing the first engine as a function of a fluid sample of the first engine; comparing the sample signature to a plurality of reference signatures obtained from reference engines belonging to a common family with the first engine; selecting ones of the reference engines for which a difference between a corresponding reference signature and the sample signature is below a threshold; and providing historical engine data of the selected ones of the reference engines as a basis for evaluating the condition of the first engine; wherein the difference between the corresponding reference signature and the sample signature is found using: ∑ i = 1 n W Ti ( D Ei - D Ci ) 2 D Ei + D Ci where n is a number of data sets of the fluid sample, W Ti is a total weight assigned to a data set i, D Ei is a value of a data set i of the sample signature, and D Ci is a value of a data set i of the reference signature. 11. The system as claimed in claim 10 , wherein the first engine and the reference engines belong to a common family when they correspond to a same engine model. 12. The system of claim 10 , wherein providing historical engine data of the selected ones of the reference engines comprises providing a total number of operating hours of the selected ones of the reference engines. 13. The system of claim 10 , wherein providing historical engine data of the selected ones of the reference engines comprises identifying which portion of the selected ones of the reference engines ran without any problems for at least one total number of operating hours. 14. The system of claim 10 , wherein the program instructions are further executable for estimating a condition of the first engine based on the historical data of the selected ones of the reference engines. 15. The system of claim 10 , wherein obtaining a sample signature of the first engine comprises obtaining fluid sample data and generating the sample signature. 16. The system of claim 15 , wherein generating the sample signature comprises assigning weights to different data sets of the fluid sample data, each one of the data sets corresponding to one or more classes of materials into which particles of the sample fluid are sorted. 17. The system of claim 16 , wherein assigning weights to different data sets comprises
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