Anomaly detection

1. A non-transitory computer readable medium storing a computer program, wherein when the computer program is executed by one or more processors of a computing device, the computer program performs methods for processing input data, and the methods include:

obtaining input data based on sensor data obtained during manufacturing of an article using one or more manufacturing recipes in a plurality of manufacturing equipment or using a plurality of manufacturing recipes in one or more manufacturing equipment;

feeding the input data and additional information for identifying one or more contexts about the input data into a neural network model loaded on a computer device, wherein the neural network model, including an encoder and a decoder, is trained with the input data and the additional information;

feeding a context indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a first preprocessing neural network model;

processing the context indicator using the first preprocessing neural network model;

further feeding a preprocessed context indicator which is an output of the first preprocessing neural network model, into the neural network model,

wherein the preprocessed context indicator is a dense representation of the context indicator;

generating an output by processing the input data using the neural network model based on the additional information about the input data, wherein the neural network model processes the input data for different contexts of the input data identified by the additional information; and

detecting an anomaly for a plurality of normal states corresponding to the input data based on the output of the neural network model.

2. The non-transitory computer readable medium according to claim 1 , wherein:

the context indicator associates the input data with at least one of one manufacturing recipe of the one or more manufacturing recipes or one manufacturing equipment of the one or more manufacturing equipment, and wherein feeding the context indicator comprises feeding into the neural network model by matching with the input data.

3. The non-transitory computer readable medium according to claim 2 , wherein the neural network model, implemented by the one or more processors, is configured to process each input data based on the context indicator matched with the each input data.

4. The non-transitory computer readable medium according to claim 3 , wherein the neural network model processes the each input data by specifying one or all of one manufacturing equipment of the one or more manufacturing equipment and one manufacturing recipe of the one or more manufacturing recipes, based on the each context indicator matched with the each input data.

5. The non-transitory computer readable medium according to claim 2 , wherein the context indicator includes a one hot vector that includes a sparse representation of at least one of one manufacturing recipe of the one or more manufacturing recipes or one manufacturing equipment of the one or more manufacturing equipment.

6. The non-transitory computer readable medium according to claim 2 , wherein the further feeding the context indicator into the neural network model by matching with the input data includes:

feeding the context indicator matched with the input data into an input layer or an intermediate layer of the neural network model.

7. The non-transitory computer readable medium according to claim 1 , wherein the further feeding a preprocessed context indicator, which is an output of the first preprocessing neural network model, into the neural network model includes:

feeding the preprocessed context indicator into an input layer or an intermediate layer of the neural network model.

8. The non-transitory computer readable medium according to claim 1 , wherein the methods further include:

further feeding a context characteristic indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into the neural network model by matching with the input data.

9. The non-transitory computer readable medium according to claim 8 , wherein the neural network model, implemented by the one or more processors, is configured to process each input data based on each context characteristic indicator matched with each input data.

10. The non-transitory computer readable medium according to claim 9 , wherein the neural network model processes the each input data based on material characteristic information of the article that is obtained based on the each context characteristic indicator matched with the each input data.

11. The non-transitory computer readable medium according to claim 8 , wherein the context characteristic indicator includes a vector representation of at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment.

12. The non-transitory computer readable medium according to claim 8 , wherein the further feeding a context characteristic indicator into the neural network model by matching with the input data, includes:

feeding the context characteristic indicator matched with the input data into an input layer or an intermediate layer of the neural network model.

13. The non-transitory computer readable medium according to claim 1 , wherein the methods further include:

feeding a context characteristic indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a second preprocessing neural network model;

processing the context characteristic indicator using the second preprocessing neural network model; and

further feeding a preprocessed context characteristic indicator which is an output of the second preprocessing neural network model, into the neural network model,

wherein the preprocessed context characteristic indicator is a dense representation of the context characteristic indicator.

14. The non-transitory computer readable medium according to claim 13 , wherein the further feeding a preprocessed context characteristic indicator, which is an output of the second preprocessing neural network model, into the neural network model includes:

feeding the preprocessed context characteristic indicator into an input layer or an intermediate layer of the neural network model.

15. The non-transitory computer readable medium according to claim 1 , wherein the neural network model is a neural network model capable of processing all or one of encoding and decoding of the input data.

16. The non-transitory computer readable medium according to claim 1 , wherein the anomaly includes all or one of an article anomaly of the article and manufacturing equipment anomaly of the one or more manufacturing equipment.

17. The non-transitory computer readable medium according to claim 1 , wherein the anomaly includes a manufacturing anomaly detected by a sensor data when the article is produced in the one or more manufacturing equipment.

18. The non-transitory computer readable medium according to claim 1 , wherein the neural network model includes a neural network function selected from the group consisting of an AutoEncoder (AE), a Denoising AutoEncoder (DAE), or a Variational AutoEncoder (VAE).

19. The non-transitory computer readable medium according to claim 1 , wherein the one or more manufacturing recipes includes an operating parameter of the manufacturing equipment for producing the article that is loaded on the one or more manufacturing equipment.

20. The non-transitory computer readable medium according to claim 1 , wherein one input data comprises sensor data obtained during manufacturing of an article by using one manufacturing recipe of the one or more manufacturing recipes in one manufacturing equipment of the one or more manufacturing equipment.

21. A method of processing input data, the method comprising:

obtaining input data based on sensor data obtained during manufacturing of an article using one or more manufacturing recipes in a plurality of manufacturing equipment or using a plurality of manufacturing recipes in one or more manufacturing equipment;

feeding the input data and additional information for identifying one or more contexts about the input data into a neural network model loaded on a computer device, wherein the neural network model, including an encoder and a decoder, is trained with the input data and the additional information;

feeding a context indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a first preprocessing neural network model;

processing the context indicator using the first preprocessing neural network model;

further feeding a preprocessed context indicator which is an output of the first preprocessing neural network model, into the neural network model,

wherein the preprocessed context indicator is a dense representation of the context indicator;

generating an output by processing the input data using the neural network model based on the additional information about the input data, wherein the neural network model processes the input data for different contexts of the input data identified by the additional information; and

detecting an anomaly for a plurality of normal states corresponding to the input data based on the output of the neural network model.

22. A computing device for processing input data, comprising:

one or more processors; and

a memory for storing computer programs executable on the one or more processors;

wherein the one or more processors are configured to:

obtain input data based on sensor data obtained during manufacturing of an article using one or more manufacturing recipes in a plurality of manufacturing equipment or using a plurality of manufacturing recipes in one or more manufacturing equipment;

feed the input data and additional information for identifying one or more contexts about the input data into a neural network model loaded on a computer device, wherein the neural network model, including an encoder and a decoder, is trained with the input data and the additional information;

feed a context indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a first preprocessing neural network model;

process the context indicator using the first preprocessing neural network model;

further feed a preprocessed context indicator which is an output of the first preprocessing neural network model, into the neural network model,

wherein the preprocessed context indicator is a dense representation of the context indicator;

generate an output by processing the input data using the neural network model based on the additional information about the input data, wherein the neural network model processes the input data for different contexts of the input data identified by the additional information; and

detect an anomaly for a plurality of normal states corresponding to the input data based on the output of the neural network model.