Error-based method for calculating a remaining useful life of an apparatus

Patent metadata

Field	Value
Publication number	US-11726469-B2
Application number	US-202117342693-A
Country	US
Kind code	B2
Filing date	Jun 9, 2021
Priority date	Jul 8, 2020
Publication date	Aug 15, 2023
Grant date	Aug 15, 2023

Abstract

A method for calculating a remaining useful life of an apparatus comprises the following steps. Time-series of previous runs and a current run of the apparatus are provided containing data of sensors configured to monitor parameters of the apparatus. An error occurs when a parameter breaches a threshold. Cumulative counts of errors occurring during a run are calculated. A linearly decreasing remaining useful life is calculated for previous runs. Breakdowns of the apparatus are mapped in an error space. Each dimension of the error space refers to one type of an error. The breakdown points are mapped at coordinates which represent cumulative error counts at the time of the breakdowns. A test point representing cumulative error counts of the current run is mapped. At least two nearest breakdown points to the test point are identified. The remaining useful life of the apparatus is calculated based on the nearest breakdown points.

First claim

What is claimed is:

1. A computer implemented error-based method for calculating a remaining useful life of an apparatus comprising:
providing acquired time-series of previous runs of the apparatus containing data of a plurality of sensors of the apparatus and acquiring a time series containing the data of the plurality of sensors with respect to a current run of the apparatus,
wherein the apparatus is a pitch system of a wind turbine,
wherein each sensor of said plurality of sensors of the apparatus is configured to monitor a parameter of the apparatus or a parameter of an environment of the apparatus, respectively,
wherein cumulative counts of errors occurring during a run of the apparatus are calculated for each type of an error and provided within the time series,
wherein the error occurs when the parameter of the apparatus or the parameter of the environment of the apparatus as monitored by a sensor breaches a pre-defined threshold,
wherein a linearly decreasing remaining useful life for each previous run of the apparatus indicating breakdowns of the apparatus by a value of zero is calculated and provided within the time series,
mapping the breakdowns of the apparatus as points in an error space,
wherein each dimension of the error space refers to one type of the error, respectively,
wherein breakdown points are mapped at coordinates which represent cumulative error counts at times of the breakdowns,

mapping a test point in the error space, wherein the test point represents the cumulative error counts for the apparatus in operation,
identifying at least two nearest breakdown points with respect to the test point by calculating and comparing distances of the test point to all of the breakdown points mapped in the error space,
calculating the remaining useful life of the apparatus which is a function of a quotient of a distance of the test point to an origin of the error space and a mean distance of the at least two nearest breakdown points to the origin.

2. The computer implemented error-based method according to claim 1 , wherein the remaining useful life is given by a product of a mean operation time of all available runs of the apparatus and a difference between the quotient and one.

3. The computer implemented error-based method according to claim 1 , wherein relative error weights are considered for mapping the points in the error space.

4. The computer implemented error-based method according to claim 3 , wherein the relative error weights are based on temporal distances between the errors and the breakdowns.

5. The computer implemented error-based method according to claim 4 , wherein considering the relative error weights based on the temporal distances between the errors and the breakdowns, further comprises
determining remaining times until a breakdown of the apparatus for said each type of said error, wherein the remaining times are given by the linearly decreasing remaining useful life at each time a number of errors is increasing by the value of one,
calculating the relative error weights based on the remaining times for said each type of said error, mapping the points in the error space at weighted coordinates.

6. The computer implemented error-based method according to claim 3 , wherein the relative error weights are based on frequencies of the errors occurring.

7. The computer implemented error-based method according to claim 6 , wherein considering the relative error weights based on the frequencies of the errors occurring, further comprises
determining remaining times until a breakdown of the apparatus for each type of said error,
wherein the remaining times are given by the linearly decreasing remaining useful life at each time a number of errors is increasing by the value of one,

calculating parameters based on the remaining times for each type of said error, respectively,
calculating the relative error weights based on the parameters, wherein each relative error weight for a specific type of said error is indicative of a frequency of the error occurring,
mapping the points in the error space at weighted coordinates in the error space.

8. The computer implemented error-based method according to claim 5 ,
wherein total times are calculated for each type of said error by summing up the remaining times of a corresponding error,
wherein each relative error weight for a specific type of said error is a logarithm of a quotient of a sum of the total times of all types of said errors and a total time of the corresponding error.

9. The computer implemented error-based method according to claim 1 , wherein the time series include information about the previous runs of at least one further apparatus of a same type as the apparatus equipped with a second plurality of sensors.

10. The computer implemented error-based method according to claim 1 , further comprising
determining categories comprising different categories for different time slots according to the remaining useful life as calculated,
performing a supervised training of a machine learning algorithm using the categories or numeric values of the remaining useful life as calculated as target variables of a training data set, wherein the training data set contains the time series.

11. The computer implemented error-based method according to claim 10 , further comprising
predicting a category by the machine learning algorithm based on sensor data and error data of the apparatus in operation and the remaining useful life that is calculated of the previous runs.

12. A system comprising a plurality of sensors and means designed to perform method steps of a computer implemented error-based method for calculating a remaining useful life of an apparatus, said computer implemented error-based method comprising:
providing acquired time-series of previous runs of the apparatus containing data of the plurality of sensors of the apparatus and acquiring a time series containing the data of the plurality of sensors with respect to a current run of the apparatus,
wherein the apparatus is a pitch system of a wind turbine,
wherein each sensor of said plurality of sensors of the apparatus is configured to monitor a parameter of the apparatus or a parameter of an environment of the apparatus, respectively,
wherein cumulative counts of errors occurring during a run of the apparatus are calculated for each type of an error and provided within the time series,
wherein the error occurs when the parameter of the apparatus or the parameter of the environment of the apparatus as monitored by a sensor breaches a pre-defined threshold,
wherein a linearly decreasing remaining useful life for each previous run of the apparatus indicating breakdowns of the apparatus by a value of zero is calculated and provided within the time series,

mapping the breakdowns of the apparatus as points in an error space,
wherein each dimension of the error space refers to one type of the error, respectively,
wherein breakdown points are mapped at coordinates which represent cumulative error counts at times of the breakdowns,

mapping a test point in the error space, wherein the test point represents the cumulative error counts for the apparatus in operation,
identifying at least two nearest breakdown points with respect to the test point by calculating and comparing distances of the test point to all of the breakdown points mapped in the error space,
calculating the remaining useful life of the apparatus which is a function of a quotient of a distance of the test point to an origin of the error space and a mean distance of the at least two nearest breakdown points to the origin.

13. A non-

Assignees

• Atos Global It Solutions And Services Private Ltd
• Bull Sas

Inventors

• Ajmera Abhinav

Classifications

• G05B23/0283 Primary

  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 title

• F03D17/00

  Monitoring or testing of wind motors, e.g. diagnostics (testing during commissioning of wind motors F03D13/30) · CPC title

• G06N20/00

  Machine learning · CPC title

• G05B23/0237 Primary

  based on parallel systems, e.g. comparing signals produced at the same time by same type systems and detect faulty ones by noticing differences among their responses · CPC title

• G05B23/024

  Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks · CPC title