Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data

Abstract

<div>Abstract—An increasing number of industrial assets are equipped with IoT sensor platforms and the industry now expects data-driven maintenance strategies with minimal deployment costs. However, gathering labeled training data for supervised tasks such as anomaly detection is costly and often difficult to implement in operational environments. Therefore, this work aims to design and implement a solution that reduces the required amount of data for training anomaly classification models on time series sensor data and thereby brings down the overall deployment effort of IoT anomaly detection sensors. We set up several in-lab experiments using three peristaltic pumps and investigated approaches for transferring trained anomaly detection models across assets of the same type. Our experiments achieved promising effectiveness and provide initial evidence that transfer learning could be a suitable strategy for using pretrained anomaly classification models across industrial assets of the same type with minimal prior labeling and training effort. This work could serve as a starting point for more general, pretrained sensor data embeddings, applicable to a wide range of assets.</div>

Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

“…While some traditional methods for fault detection require feature engineering (Kato, Yairi, & Hori, 2001;Su, Sun, Gao, Qiu, & Tian, 2019;J. Wang et al, 2020), recent work has shown that end-to-end autoencoders can outperform traditional approaches (Zong et al, 2018;Maleki, Maleki, & Jennings, 2021;Heistracher, Jalali, Suendermann, et al, 2021). Autoencoders are promising and have been for minimal-configuration fault detection (Heistracher, Jalali, Suendermann, et al, 2021;Hood et al, 2021), however this unsupervised methods lack the ability for fault classification or fault location.…”