Modeling Machine Health Using Gated Recurrent Units with Entity Embeddings and K-Means Clustering
| Autor: | Guruprasad Sosale, Subanatarajan Subbiah, Diego Pareschi, Ralf Gitzel, Ido Amihai, Moncef Chioua, Arzam Kotriwala |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Training set Artificial neural network business.industry Computer science 020208 electrical & electronic engineering k-means clustering Pattern recognition 02 engineering and technology Neural network classifier Metadata 020901 industrial engineering & automation Logic gate 0202 electrical engineering electronic engineering information engineering Neural network architecture Artificial intelligence Cluster analysis business |
| Zdroj: | INDIN |
| DOI: | 10.1109/indin.2018.8472065 |
| Popis: | We describe a machine learning approach for predicting machine health indicators two weeks into the future. The model developed uses a neural network architecture that incorporates sensor data inputs using gated recurrent units with metadata inputs using entity embeddings. Both inputs are then concatenated and fed to a fully connected neural network classifier. Furthermore, our classes are generated by clustering the continuous sensor values of the training data using K-Means. To validate the model we performed an ablation study in order to verify the effectiveness of each of the model’s components, and also compared our approach to the typical method of predicting continuous scalar values. |
| Databáze: | OpenAIRE |
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