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.
@article{arxiv.2110.04049,
title = {Minimal-Configuration Anomaly Detection for IIoT Sensors},
author = {Clemens Heistracher and Anahid Jalali and Axel Suendermann and Sebastian Meixner and Daniel Schall and Bernhard Haslhofer and Jana Kemnitz},
journal= {arXiv preprint arXiv:2110.04049},
year = {2021}
}
Comments
This paper is accepted at the Industrial Track IDSC https://idsc.at/. The link to the publication and final version will follow as so the paper is published by Springer