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Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability
- Source :
- CASE
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- 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.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Artificial neural network
Computer Science - Artificial Intelligence
I.2.6
Computer science
business.industry
Reliability (computer networking)
Supervised learning
G.3
Machine learning
computer.software_genre
Autoencoder
Machine Learning (cs.LG)
I.2.1
Artificial Intelligence (cs.AI)
Principal component analysis
Anomaly detection
Isolation (database systems)
Artificial intelligence
Time series
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
- Accession number :
- edsair.doi.dedup.....10d62275dc13cec48acf480dc150377b
- Full Text :
- https://doi.org/10.1109/case49439.2021.9551636