anomaly detection usiing VELC: A New Variational AutoEncoder Based Model

We have tried implemented the research paper "VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection"

Paper link :- https://arxiv.org/abs/1907.01702 Model file :- velc.py

anomaly detection using vae lstm with a re-encoder

Along with the VELC model we have also implement the time series anomaly detection using VAE where the encoder, decoder and a re-encoder layers, which are Bi-directional LSTMs. Model :- vae_with_ReEncoder.py

anomaly detection using simple vae lstm

Along with the VELC model we have also implement the time series anomaly detection using VAE where the encoder and a decoder layers, which are Bi-directional LSTMs. Model :- simple_vae_lstm_model.py

Data

The code uses NASA bearing data set for training and test. The bearing data has been uploaded to the folder named "dataset" here itself in the repository.

Dataset link :- https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/

We have done some pre-processing and the final output "Bearing_dataset.csv" datatset file that is used by model is present in "dataset" folder. To Show some insights about the raw data, we have generated some graphs as well. Code that is used in pre-processing and to generate insights about data is also available in "pre_processing_insights.py".

References

https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770

https://towardsdatascience.com/how-to-use-machine-learning-for-anomaly-detection-and-condition-monitoring-6742f82900d7

https://towardsdatascience.com/variational-autoencoders-as-generative-models-with-keras-e0c79415a7eb

https://github.com/shaohua0116/VAE-Tensorflow

Code uses the rest of the folders to save model and the Images.