# Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants

arXiv: https://arxiv.org/abs/2302.02896 · HTML: https://mlsanity.com/paper/2302.02896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders](https://mlsanity.com/paper/1709.00663/) (2017)
- [LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data](https://mlsanity.com/paper/2204.06701/) (2022)
- [Dual Adversarial Auto-Encoders for Clustering](https://mlsanity.com/paper/2008.10038/) (2020)
- [Generative Modeling with Conditional Autoencoders: Building an Integrated Cell](https://mlsanity.com/paper/1705.00092/) (2017)

## Built on by (most cited first, 1 of 1)

- [Anomaly Detection in Power Generation Plants with Generative Adversarial Networks](https://mlsanity.com/paper/2310.00335/) (2023)

## Abstract (arXiv)

One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including telecommunication industries, those are still experiencing a non-stable electricity supply. Therefore, they have to rely on generators to guarantee their full functionality. Those generators depend on fuel to function and the rate of consumption gets usually high, if not monitored properly. Monitoring operation is usually carried out by a (non-expert) human. In some cases, this could be a tedious process, as some companies have reported an exaggerated high consumption rate. This work proposes a label assisted autoencoder for anomaly detection in the fuel consumed by power generating plants. In addition to the autoencoder model, we added a labelling assistance module that checks if an observation is labelled, the label is used to check the veracity of the corresponding anomaly classification given a threshold. A consensus is then reached on whether training should stop or whether the threshold should be updated or the training should continue with the search for hyper-parameters. Results show that the proposed model is highly efficient for reading anomalies with a detection accuracy of 97.20% which outperforms the existing model of 96.1% accuracy trained on the same dataset. In addition, the proposed model is able to classify the anomalies according to their degree of severity.