Determining a machine condition

• US 9,946,232 B2
• Filed: 06/19/2012
• Issued: 04/17/2018
• Est. Priority Date: 06/19/2012
• Status: Active Grant

• Current Assignee: GKN Aerospace Sweden AB (Melrose Industries Plc)
• Original Assignee: GKN Aerospace Sweden AB (Melrose Industries Plc)
• Inventors: Nilsson, Mikael, Norder, Daniel
• Primary Examiner(s): Wang, Philip
• Application Number:
  US14/407,634
• Publication Number:
  US 20150234365A1
• Time in Patent Office: 2,128 Days
• Field of Search: None
• CPC Class Codes: F01K 5/02 used in regenerative installation
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

A set of load data for a selected point in time and resulting from the machine operation is received. The load data is provided from a first database comprising predefined machine conditions associated to different sets of load data for the machine. One of the predefined machine conditions that is most representative of the received set of load data is selected.

Claims

1. A method, comprising:

receiving a set of load data, for a selected point in time, the load data including data from operation of a machine;

from a first database comprising predefined machine conditions associated to different sets of load data for said machine, selecting a predefined machine condition that is most representative of said received set of load data; and

retrieving, from a set of pre-calculated machine condition values for each of said predefined machine conditions in said first database, a set of machine condition parameter values corresponding to the selected machine condition;

wherein said pre-calculated machine condition values comprise machine condition values based on previously measured load data and machine condition values based on interpolated load data.

2. The method of claim 1, wherein said set of load data comprises values of a plurality of time dependent performance parameters measured at a selected point in time during machine operation.

3. The method of claim 1, wherein each machine condition in said first database comprise a unique identifier, the method further comprising:

providing an output comprising an identifier for the machine condition most representative of said set of load data and information identifying a machine session.

4. The method of claim 1, wherein said first database further comprises a steady state condition corresponding to each machine condition, each steady state condition being represented by a set of load data performance parameter values.

5. The method of claim 1, wherein:

a second database comprises the set of pre-calculated machine condition values for each of said predefined machine conditions in said first database.

6. The method of claim 1, wherein selecting the predefined machine conditions that is most representative of said received set of load data comprises:

selecting a machine condition from said first database by matching a subset of said load data with corresponding steady state condition values;

defining a subset of steady state conditions comprising said selected steady state condition and a plurality of surrounding steady state conditions based on a tolerance range of at least one parameter value of said subset of load data;

calculating the relative differences between each parameter value of said subset of load data and corresponding parameter values for each of said subset of steady state conditions;

adding said relative differences together for each steady state condition; and

selecting the steady state condition having the smallest total difference.

7. The method of claim 6, wherein, if two or more steady state conditions have a same relative difference, a steady state condition corresponding to measured load data is selected over a steady state condition corresponding to interpolated load data.

8. The method of claim 1, wherein said selected point in time is selected based on a predetermined selection criterion.

9. The method of claim 8, wherein said predetermined selection criterion is a selection frequency for selecting a plurality of sets of load data at a regular time-interval.

10. The method of claim 1, wherein the step of receiving a set of load data comprises verifying that said load data are within a predetermined range.

11. The method of claim 1, wherein said load data comprises measured values of performance parameters influencing a mechanical life length of components in said machine.

12. The method of claim 11, wherein said performance parameters comprises at least one of vibration, stress, strain, engine revolutions per minute, and ambient temperature.

13. The method of claim 1, wherein said machine condition comprises at least one of engine pressure, temperature, mass flow, and torque.

14. The method of claim 1, wherein said machine is an aircraft engine, and said load data comprises aircraft altitude and aircraft velocity.

15. The method of claim 1, further comprising predicting the life consumption for said machine component based on the selected determined machine condition.

16. The method of claim 15, wherein predicting life consumption of a component further comprises:

calculating at least one of stresses, strains and temperature for a critical area of said component based on said determined machine condition; and

predicting life consumption of said component for said load data based on said at least one of the calculated stresses, strains and temperatures.

17. A system, comprising:

a first database comprising predefined machine conditions associated to different sets of load data for a machine;

wherein said system includes a computer that is programmed to;

receive a set of load data, for a selected point in time, resulting from said machine operation; and

selecting a predefined machine condition that is most representative of said received set of load data; and

retrieving, from a set of pre-calculated machine condition values for each of said predefined machine conditions in said first database, a set of machine condition parameter values corresponding to said selected machine condition;

wherein said pre-calculated machine condition values comprise machine condition values based on previously measured load data and machine condition values based on interpolated load data.

18. The system of claim 17, further comprising a second database comprising the set of pre-calculated machine condition values for each of said predefined machine conditions in said first database.

19. A method, comprising:

receiving a set of load data, for a selected point in time, the load data including data from operation of a machine;

from a first database comprising predefined machine conditions that each are associated to different sets of load data for said machine and that further comprises a steady state condition corresponding to each machine condition, each steady state condition being represented by a set of load data performance parameter values, selecting a predefined machine condition that is most representative of said received set of load data;

selecting a machine condition most representative of said set of load data and information identifying a machine session; and

from a set of pre-calculated machine condition values for each of said predefined machine conditions in said first database, retrieving a set of machine condition parameter values corresponding to said selected machine condition;

wherein selecting the predefined machine conditions that is most representative of said received set of load data includes;

selecting a machine condition from said first database by matching a subset of said load data with corresponding steady state condition values;

defining a subset of steady state conditions comprising said selected steady state condition and a plurality of surrounding steady state conditions based on a tolerance range of at least one parameter value of said subset of load data;

calculating the relative differences between each parameter value of said subset of load data and corresponding parameter values for each of said subset of steady state conditions;

adding said relative differences together for each steady state condition; and

selecting the steady state condition having the smallest total difference.