Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

Dynamic VAE frame

1. Automatic feature extraction can be achieved by probability distribution of battery data

The application of data science method to anomaly discrimination in time series is limited. The main reason is that exception tags are usually few in quantity, low in quality, mismarked or omitted. In order to solve this problem, we hope to process a large number of time series data that have not been manually filtered through the compilation and interpretation model related to information theory and the use of large-scale networks (such as Transformer) to parameterize the probability distribution of data and the correlation function on each feature.
The parameters and network structure acquired by learning contain highly nonlinear features that are difficult to be extracted artificially, which can help existing models to achieve better performance in anomaly and health prediction tasks.

2. A simple model is used to detect anomalies in extracted features

Purpose of model

1. For feature extraction of battery data
2. According to the extracted features, the anomaly detection model is learned.

How to get data

cd DATA
wget http://82.156.209.173/s/6Saazbbxq92iez7/download
unzip download
cd dataset/dahu
tar -xf test_mulmileage.tar

You can download and decompress the files to the DATA/ directory according to the provided data link, put the data folder in the DATA subdirectory.

How to run：

python train.py

You need to switch to the root directory of the project and run Python train.py. The network will generate the features of the data set extracted under the current time and store them in the feature folder, store the model structure of the network in the Model folder, and store the loss changes during training in the Loss folder.