Semi-supervised Fault Detection using Convolutional Clustering
- Performed exploration and feature engineering on Tennessee Eastman Process data
- Trained a convolutional autoencoder with K-Means clustering algorithm incorporated with the network criterion
- Extracted the encoder to train a new model in a semi-supervised manner
- Developed different algorithms for binary and multi-class classification
- Tuned the hyperparameters and visualized the results using Tensorboard