# Minimal-Configuration Anomaly Detection for IIoT Sensors

arXiv: https://arxiv.org/abs/2110.04049 · HTML: https://mlsanity.com/paper/2110.04049/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications](https://mlsanity.com/paper/2304.11101/) (2023)
- [Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems](https://mlsanity.com/paper/2210.08011/) (2022)
- [Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets](https://mlsanity.com/paper/2305.06753/) (2023)

## Abstract (arXiv)

The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment. Recent advances in deep learning, especially long-short-term memory (LSTM) and autoencoders, offer promising methods for detecting anomalies in sensor data recordings. We compared autoencoders with various architectures such as deep neural networks (DNN), LSTMs and convolutional neural networks (CNN) using a simple benchmark dataset, which we generated by operating a peristaltic pump under various operating conditions and inducing anomalies manually. Our preliminary results indicate that a single model can detect anomalies under various operating conditions on a four-dimensional data set without any specific feature engineering for each operating condition. We consider this work as being the first step towards a generic anomaly detection method, which is applicable for a wide range of industrial equipment.