The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, Echo State Networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this paper, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best trade-off in terms of memory footprint and inference time.


Computer Science

Keywords and Phrases

Anomaly detection; edge computing; ESN; industry 4.0; intelligent CPS

International Standard Serial Number (ISSN)

1941-0050; 1551-3203

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2022