Minimal-Configuration Anomaly Detection for IIoT Sensors
This work addresses the need for easy-to-deploy anomaly detection solutions in industrial settings, but it is incremental as it builds on existing deep learning methods without introducing major innovations.
The paper tackled the problem of minimal-configuration anomaly detection for industrial IoT sensors by comparing autoencoders with various deep learning architectures on a generated benchmark dataset, achieving preliminary results where a single model detected anomalies across different operating conditions without specific feature engineering.
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.