Method for monitoring the oil system of a turbomachine

• US 8,676,436 B2
• Filed: 11/14/2011
• Issued: 03/18/2014
• Est. Priority Date: 11/24/2010
• Status: Active Grant

• Current Assignee: Techspace Aero SA (Safran SA)
• Original Assignee: Techspace Aero SA (Safran SA)
• Inventors: Raimarckers, Nicolas, Cornet, Albert, Charlier, Jacques, Strengnart, Marc
• Primary Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US13/295,538
• Publication Number:
  US 20120130617A1
• Time in Patent Office: 855 Days
• Field of Search: 701/29.1, 701/29.4, 701/29.5, 701/31.4, 701/32.8, 701/32.9, 701/34.4, 701/99, 703/2, 60/390.8, 706/21, 706/46, 706/47, 73/530.5, 73/530.7, 731/180.1, 731/180.2, 731/145.5
• US Class Current: 701/29.5
• CPC Class Codes: F01D 21/003 Arrangements for testing or measuring for measuring vibrations G01H
• CPC Class Codes: F01D 25/18 Lubricating arrangements of machines or engines in general F01M
• CPC Class Codes: F02C 7/06 Arrangements of bearings bearings F16C; Lubricating of turbo machines F01D25/18; of machines or engines in general F01M
• CPC Class Codes: F05D 2260/80 Diagnostics
• CPC Class Codes: F05D 2270/11 to prolong engine life
• CPC Class Codes: F05D 2270/44 active, predictive, or anticipative

Abstract

A monitoring method that includes calculating the autonomy of a lubrication system of an airplane engine and also providing for the diagnosis and prognosis of a plurality of problems and breakdowns of the engine and of its lubrication system by means of measurements taken by sensors arranged in the lubrication system. The method further comprises the step of calculating the status of the lubrication system at a given moment and calculating its evolution over time in order to determine the remaining lifetime before a breakdown.

Claims

1. A monitoring method comprising:

calculating the autonomy of a lubrication system of an airplane engine and further allowing the diagnosis and prognosis of a plurality of problems and breakdowns of the engine and of its lubrication system by means of measurements taken by sensors arranged in said lubrication system, the method further comprising the step of calculating a status of the lubrication system at a given moment and calculating its evolution over time in order to determine the remaining lifetime before a breakdown, wherein data coming in from the sensors are merged with data coming from reference models to validate a detection of a problem or breakdown, and wherein the reference models are a gulping model, a debris-generation model, a model for oil-tank level based on the content, a performance model for a pump, a pressure-difference model at the terminals of a filter, and a heat-generation model in the enclosure.

2. The method as in claim 1, further comprising doing away with the parasitic effects influencing the measurements by comparing the signals of two engines of the airplane, possibly with a reference value, said engines being under substantially identical conditions, and/or by comparing two phases of a same flight or of different flights for which the parasitic effects are identical.

3. The method as in claim 1, wherein said sensors respectively measure an oil level, an oil temperature, an oil pressure, a differential pressure at the terminals of a filter, the presence of debris in the oil, and the quality of the oil.

4. The method as in claim 1, being used for the in-flight management of the airplane and for managing the maintenance of the airplane and being able to diagnose and forecast an oil leak, aging, breaking and overheating of bearings contained in an enclosure of the lubrication system, coking of the oil in tubing, dirtying/clogging in an oil-supply circuit, fuel leak in the oil and deterioration of the quality of the oil.

5. The method as in claim 1, wherein, by increasing the order of precision of the calculation of the status, evolution and autonomy of the system, the measurements are taken as follows:

the measurements are taken at the beginning and end of the flight and the status, evolution and autonomy are calculated based on those measurements;

or the measurements are taken at the beginning and end of each flight phase and the status, evolution and autonomy are calculated based on those measurements for each flight phase;

or the measurements are taken several times per flight phase and the status, evolution and autonomy are calculated based on those measurements for each flight phase;

or the measurements are taken several times per flight phase as well as during the transitions separating the flight phases and the status, evolution and autonomy are calculated based on those measurements for each flight phase and during the transitions.

6. The method as in claim 1, comparing parameters of the models for status calculation and/or parameters of the models for evolution calculation with parameters of reference models in order to detect abnormal behavior.

7. The method as in claim 6, wherein several data recordings related to the flight in progress and to the preceding flights are done to initialize the parameters of the models, to initialize calibration parameters of the sensors, to compare the parameters of the models for calculating the status and the evolution and/or the calibration parameters with those of the reference models and thus to identify a drift, to update the parameters of the models based on the models for recent flights in order to take into account the aging of the engine and to continuously improve the various parameters owing to feedback.

8. The method as in claim 6, wherein an alarm is triggered when the status has reached a critical level and/or when the evolution is abnormal and/or when a drift of the parameters of the models for status calculation and/or the parameters of the models for evolution calculation is observed and/or when the autonomy is insufficient.

9. The method as in claim 1, returning the status of the lubrication system to a higher monitoring system of an engine or of the airplane.

10. The method as in claim 1, wherein data coming from the sensors and reference models are also merged with data from models for parasitic effects to validate a detection of a problem or a breakdown.

11. The method as in claim 10, wherein:

data on the engine rating and coming from the performance model of a pump are merged with the measurements of oil temperature and pressure to validate the detection of clogged elements of the supply circuit such as the filter, injectors and exchangers;

and/or data on the engine rating and coming from the performance model of a pump and from the model of pressure difference at the terminals of the filter are merged with the measurements of oil temperature and pressure difference at the terminals of the filter to validate the detection of clogged filter;

and/or data on the engine rating and coming from the performance model of a pump are merged with the measurements of oil temperature and pressure as well as with the measurements of pressure difference at the terminals of the filter to distinguish the clogging of the filter from the clogging of another element of the supply circuit;

and/or data on the engine rating and coming from the heat-generation model in the enclosure are merged with the measurements of oil temperature to validate the detection of abnormal heat generation;

and/or data on the engine rating and coming from the heat-generation model in the enclosure are merged with the measurements of oil temperature, debris and oil-quality sensor to validate the detection of a problem in the bearings;

and/or the data on the rotation speed of a high-pressure shaft of the engine and coming from models of parasitic effects are merged with the measurements of oil temperature and oil-level sensor to validate the detection of an oil leak;

and/or the data on the rotation speed of a high-pressure shaft of the engine and coming from the models of parasitic effects are merged with the measurements of oil temperature, oil-level sensor and oil-quality sensor to validate the detection of a fuel leak in the oil.

12. A monitoring method comprising:

calculating the autonomy of a lubrication system of an airplane engine and further allowing the diagnosis and prognosis of a plurality of problems and breakdowns of the engine and of its lubrication system by means of measurements taken by sensors arranged in said lubrication system, the method further comprising the step of calculating a status of the lubrication system at a given moment and calculating its evolution over time in order to determine the remaining lifetime before a breakdown, wherein data coming in from the sensors are merged with data coming from reference models to validate a detection of breakdown; and

comparing parameters of the models for status calculation and/or parameters of the models for evolution calculation with parameters of reference models in order to detect abnormal behavior;

wherein the reference models are a gulping model, a debris-generation model, a model for oil-tank level based on the content, a performance model for a pump, a pressure-difference model at the terminals of a filter, and a heat-generation model in the enclosure.

13. The method as in claim 12, wherein the data coming from measurements taken in the lubrication system are merged with data coming from measurements taken outside the lubrication system of the engine of the airplane in order to validate the calculation of the status, evolution and/or autonomy and in order to validate the detection of a problem or breakdown.