Method for computer-implemented monitoring of a component of a wind turbine
Provided is a method for computer-implemented monitoring of a component of a wind turbine, having access to a trained machine learning model which has been trained for one or more components of the same type of wind turbines. The trained machine learning model is configured to provide an output referring to a predetermined fault occurring at a component of a wind turbine by processing vibration signals in a predetermined domain which are measured in the vicinity of the component during the operation of the wind turbine. Vibration signals are mapped to corresponding vibration signals valid for the component based on one or more given kinematic parameters of the component and one or more given kinematic parameters of another component. The machine learning model is applied to the vibration signals valid for the component, resulting in an output referring to the predetermined fault occurring at the another component.
1. A method for computer-implemented monitoring of a component of a wind turbine, where the wind turbine is a first wind turbine and the component is a first component, the method comprising:
i) operating the first wind turbine to produce vibration signals in a predetermined domain that are measured in a vicinity of the first component the operating of the first wind turbine;
ii) mapping the vibration signals to corresponding vibration signals valid for a second component of a second wind turbine based on one or more given kinematic parameters of the first component and one or more given kinematic parameters of the second component; and
iii) feeding, as an input, the corresponding vibration signals valid for the second component into a trained machine learning model, the trained machine learning model being trained for the second component of the second wind turbine, which is a same type as the first component, the second wind turbine being of another type than the first wind turbine, wherein the trained machine learning model is configured to provide an output referring to a predetermined fault occurring at the second component of the second wind turbine by processing vibration signals in a predetermined domain which are measured in a vicinity of the second component during an operation of the second wind turbine; and
iv) generating an output from the trained machine learning model indicative of a predetermined fault of the first component of the first component.
2. The method according to claim 1 , wherein the predetermined domain is the frequency domain or the cepstrum domain.
3. The method according to claim 1 , wherein the one or more given kinematic parameters of the first component are described by a same function type as the one or more given kinematic parameters of the second component but with different function parameters.
4. The method according to claim 1 , wherein the one or more given kinematic parameters of the first component are one or more specific values within the predetermined domain contained within the vibration signals measured in the vicinity of the first component in case that the predetermined fault occurs, and wherein the one or more given kinematic parameters of the second component are one or more specific values within the predetermined domain contained within the vibration signals measured in the vicinity of the second component in case that the predetermined fault occurs.
5. The method according to claim 1 , wherein the component being monitored is a part or the drivetrain of the wind turbine.
6. The method according to claim 1 , wherein the predetermined fault refers to a damage of a gearwheel or a damage of a bearing race or a damage of balls or rollers in a ball or roller bearing.
7. The method according to claim 1 , wherein the machine learning model is based on one or more neural networks or on Principal Component Analysis.
8. A computer program product, comprising a non-transitory computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement the method according to claim 1 when the program code is executed on a computer.
9. The method according to claim 1 , further comprising: storing the output and/or displaying a notification on a user interface indicative of the output.
10. An apparatus for monitoring of a component of a wind turbine, where the wind turbine is a first wind turbine and the component is a first component, the apparatus comprising:
one or more processors configured to:
operate the first wind turbine to produce vibration signals in the predetermined domain that are measured in a vicinity of the first component during an operation of the first wind turbine;
map the vibration signals to corresponding vibration signals valid for a second component of a second wind turbine based on one or more given kinematic parameters of the first component and one or more given kinematic parameters of the second component; and
feed, as an input, the corresponding vibration signals valid for the second component into a trained machine learning model, the trained machine learning model being trained for the second component of the second wind turbine, which is a same type as the first component, the second wind turbine being of another type than the first wind turbine, wherein the trained machine learning model is configured to provide an output referring to a predetermined fault occurring at the second component of the second wind turbine by processing vibration signals in a predetermined domain which are measured in a vicinity of the second component during an operation of the second wind turbine; and
generate an output from the trained machine learning model indicative of a predetermined fault of the first component of the first component; and
store the output and/or display a notification on a user interface indicative of the output.
11. The apparatus according to claim 10 , wherein the apparatus is configured to perform a method for monitoring a component of a wind turbine.
12. The apparatus according to claim 10 , wherein the one or more processors are further configured to store the output and/or display a notification on a user interface indicative of the output.