A lightweight All-MLP time–frequency anomaly detection for IIoT time series

Abstract

Anomaly detection in the Industrial Internet of Things (IIoT) aims at identifying abnormal sensor signals to ensure industrial production safety. However, most existing models only focus on high accuracy by building a bulky neural network with deep structures and huge parameters. In this case, these models usually exhibit poor timeliness and high resource consumption, which makes these models unsuitable for resource-limited edge industrial scenarios. To solve this problem, a lightweight All-MLP time–frequency anomaly detection model is proposed for IIoT time series, namely LTFAD. Firstly, unlike traditional deep and bulky solutions, a shallow and lightweight All-MLP architecture is designed to achieve high timeliness and low resource consumption. Secondly, based on the lightweight architecture, a dual-branch network is constructed to improve model accuracy by simultaneously learning “global to local” and “local to global” reconstruction. Finally, time–frequency joint learning is employed in each reconstruction branch to further enhance accuracy. To the best of our knowledge, this is the first work to develop a time–frequency anomaly detection model based only on the shallow All-MLP architecture. Extensive experiments demonstrate that LTFAD can quickly and accurately identify anomalies on resource-limited edge devices, such as the Raspberry Pi 4b and Jetson Xavier NX. The source code for LTFAD is available at https://github.com/infogroup502/LTFAD.