Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT

Abstract

The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped. These time-stamped data are the core of IIoT automation and have the potential to affect industrial processes intensely. It poses significant challenges to effectively detect anomalies from time-series data and deliver actionable insights in real time to drive improvements to industrial processes. In most practical applications, where data are used to make automated decisions, real-time anomaly detection is critical. With this focus, in this article, we advise a hybrid end-to-end deep anomaly detection (DAD) framework to accurately detect anomalies and extremely rare events on sensitive, Internet of Things (IoT) streaming data in real time or near real time. The proposed framework is based on a convolutional neural network (CNN) and a two-stage long short-term memory (LSTM)-based Autoencoder (AE). We exploit a two-stage LSTM AE in parallel to detect anomalies and extremely rare events hidden in massive sensor data by identifying short- and long-term variations in actual sensor values from the predicted values. We design and train a hybrid model using the Keras/TensorFlow framework as the backend. The experimental results on one simulation and two real datasets demonstrate that the proposed framework achieved better performance and outperforms other state-of-the-art competitive models. Moreover, to prove that the proposed model can be designed for the network edge, we train, optimize, and quantize the model to run-on resource-constrained (i.e., edge) devices. Further evaluation indicates that the training and inference time for each sample is short enough to carry out anomaly detection on edge.