Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability
This work addresses the challenge of improving anomaly detection reliability in production systems for industries seeking to optimize resources, though it is incremental as it builds on existing VAE methods.
The paper tackled the problem of anomaly detection in production systems by incorporating sparse labeled anomaly data into Variational Autoencoders, resulting in a method that outperformed other models like PCA and Isolation Forest in accuracy, precision, and recall on seven time series datasets.
Digitalization leads to data transparency for production systems that we can benefit from with data-driven analysis methods like neural networks. For example, automated anomaly detection enables saving resources and optimizing the production. We study using rarely occurring information about labeled anomalies into Variational Autoencoder neural network structures to overcome information deficits of supervised and unsupervised approaches. This method outperforms all other models in terms of accuracy, precision, and recall. We evaluate the following methods: Principal Component Analysis, Isolation Forest, Classifying Neural Networks, and Variational Autoencoders on seven time series datasets to find the best performing detection methods. We extend this idea to include more infrequently occurring meta information about production processes. This use of sparse labels, both of anomalies or production data, allows to harness any additional information available for increasing anomaly detection performance.