Minimal-Configuration Anomaly Detection for IIoT Sensors

• URL: http://arxiv.org/abs/2110.04049v1
• Date: Fri, 8 Oct 2021 11:52:52 GMT
• Title: Minimal-Configuration Anomaly Detection for IIoT Sensors
• Authors: Clemens Heistracher, Anahid Jalali, Axel Suendermann, Sebastian Meixner, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz
• Abstract summary: Low-cost IoT sensor platforms in industry boost the demand for anomaly detection solutions. Recent advances in deep learning offer promising methods for detecting anomalies in sensor data recordings. We consider this work as being the first step towards a generic anomaly detection method.
• Score: 0.2462953128215087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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