System and method for monitoring a device
A system and a method for monitoring a device including the steps of obtaining operation information from a device, wherein the operation information is associated with the condition of the device in operation; and processing the operation information with a device modelling engine to determine one or more operation conditions of the device.
1. A method for monitoring a device comprising the steps of:
obtaining operation information from a device, wherein the operation information is associated with the condition of the device in operation; and
processing the operation information with a device-specific matching network in a device modelling engine to determine or predict one or more operation conditions of the device; wherein the device modelling engine includes a plurality of device-specific matching networks each arranged for processing operation information associated with a respective one of the plurality of devices, the plurality of device-specific matching networks including the device-specific matching network; wherein each of the plurality of device-specific matching networks is generated by:
training of a base matching network with a domain data set including operation information of the plurality of devices to generate a pre-trained base matching network, and
training of the pre-trained base matching network with a target data set including operation information of the respective device.
2. The method for monitoring the device in accordance with claim 1 , wherein the based matching network is a deep neural network.
3. The method for monitoring the device in accordance with claim 2 , wherein the structure of the deep neural network is adjusted based on a stopping criterion.
4. The method for monitoring the device in accordance with claim 3 , wherein the stopping criterion is determined based on a reconstruction error obtained from the deep neural network.
5. The method for monitoring the device in accordance with claim 4 , wherein the reconstruction error is obtained by inputting the domain data set into the deep neural network.
6. The method for monitoring the device in accordance with claim 5 , wherein the structure of the deep neural network is adjusted by inserting one or more hidden layers into the deep neural network.
7. The method for monitoring the device in accordance with claim 6 , wherein the one or more hidden layers inserted by adding one or more hidden nodes into the hidden layers.
8. The method for monitoring the device in accordance with claim 7 , wherein weights or biases of the deep neural network are manipulated when the deep neural network is adjusted.
9. The method for monitoring the device in accordance with claim 1 , wherein the device is a wind turbine.
10. A system for monitoring a device comprising:
a device conditions gateway arranged to obtain operation information from a device, wherein the operation information is associated with the condition of the device in operation; and
a conditions monitoring engine arranged to process the operation information with a device-specific matching network in a device modelling engine to determine or predict one or more operation conditions of the device; wherein the device modelling engine includes a plurality of device-specific matching networks each arranged for processing operation information associated with a respective one of the plurality of devices, the plurality of device-specific matching networks including the device-specific matching network; wherein each of the plurality of device-specific matching networks is generated by:
training of a base matching network with a domain data set including operation information of the plurality of devices to generate a pre-trained base matching network, and
training of the pre-trained base matching network with a target data set including operation information of the respective device.
11. The system for monitoring the device in accordance with claim 10 , wherein the based matching network is a deep neural network.
12. The system for monitoring the device in accordance with claim 11 , wherein the structure of the deep neural network is adjusted based on a stopping criterion.
13. The system for monitoring the device in accordance with claim 12 , wherein the stopping criterion is determined based on a reconstruction error obtained from the deep neural network.
14. The system for monitoring the device in accordance with claim 13 , wherein the reconstruction error is obtained by inputting the domain data set into the deep neural network.
15. The system for monitoring the device in accordance with claim 14 , wherein the structure of the deep neural network is adjusted by inserting one or more hidden layers into the deep neural network.
16. The system for monitoring the device in accordance with claim 15 , wherein the one or more hidden layers inserted by adding one or more hidden nodes into the hidden layers.
17. The system for monitoring the device in accordance with claim 16 , wherein weights or biases of the deep neural network are manipulated when the deep neural network is adjusted.
18. The system for monitoring the device in accordance with claim 17 , wherein the device is a wind turbine.
19. The system for monitoring the device in accordance with claim 10 , further comprising a pre-processor arranged to process the operation information obtained from the device conditions gateway.
20. The system for monitoring the device in accordance with claim 19 , wherein the operation information is pre-processed before the information is inputted to the device modelling engine.
21. The system for monitoring the device in accordance with claim 20 , wherein the operation information is pre-processed to remove fault data from the operation information.
22. The system for monitoring the device in accordance with claim 10 , wherein the device conditions gateway is a SCADA system.