Evaluating Anomaly Detection Techniques in Industrial Environments: A Comparative Analysis of Autoencoders, Deep SVDD, and Supervised 2D CNNs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200445" target="_blank" >RIV/00216305:26220/26:0200445 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/pages/publications/105025964847?origin=resultslist" target="_blank" >https://www.scopus.com/pages/publications/105025964847?origin=resultslist</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3648909" target="_blank" >10.1109/ACCESS.2025.3648909</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating Anomaly Detection Techniques in Industrial Environments: A Comparative Analysis of Autoencoders, Deep SVDD, and Supervised 2D CNNs

Popis výsledku v původním jazyce

As industrial systems become increasingly complex, the need to improve operational efficiency and ensure worker safety is more urgent than ever. Radar-based monitoring offers a promising solution, but the resulting high-dimensional data presents challenges for real-time analysis and anomaly detection. In this study, we propose a radar-based anomaly detection framework built on Orthogonal Time Frequency Space (OTFS) modulation, which transforms raw radar returns into informative spatio-temporal features. Our approach integrates three deep learning models—Deep SVDD, Autoencoders, and a supervised 2D Convolutional Neural Network (CNN)—to identify abnormal movements that deviate from typical worker behavior. To boost the performance of unsupervised methods, we introduce a dynamic thresholding mechanism that adjusts to shifts in environmental conditions, improving reliability in noisy and cluttered scenes. In evaluations using real-world radar data from industrial settings, the supervised 2D CNN achieved 99.9% accuracy, while all models recorded F1-scores between 0.98 and 0.99. Notably, Deep SVDD delivered the fastest inference time at 1.71 seconds, supporting the feasibility of real-time deployment. Additionally, lightweight Transformer-based models were compared, showing comparable accuracy but higher computational cost, reaffirming the practicality of the proposed designs for edge-oriented industrial sensing.

Název v anglickém jazyce

Evaluating Anomaly Detection Techniques in Industrial Environments: A Comparative Analysis of Autoencoders, Deep SVDD, and Supervised 2D CNNs

Popis výsledku anglicky

As industrial systems become increasingly complex, the need to improve operational efficiency and ensure worker safety is more urgent than ever. Radar-based monitoring offers a promising solution, but the resulting high-dimensional data presents challenges for real-time analysis and anomaly detection. In this study, we propose a radar-based anomaly detection framework built on Orthogonal Time Frequency Space (OTFS) modulation, which transforms raw radar returns into informative spatio-temporal features. Our approach integrates three deep learning models—Deep SVDD, Autoencoders, and a supervised 2D Convolutional Neural Network (CNN)—to identify abnormal movements that deviate from typical worker behavior. To boost the performance of unsupervised methods, we introduce a dynamic thresholding mechanism that adjusts to shifts in environmental conditions, improving reliability in noisy and cluttered scenes. In evaluations using real-world radar data from industrial settings, the supervised 2D CNN achieved 99.9% accuracy, while all models recorded F1-scores between 0.98 and 0.99. Notably, Deep SVDD delivered the fastest inference time at 1.71 seconds, supporting the feasibility of real-time deployment. Additionally, lightweight Transformer-based models were compared, showing comparable accuracy but higher computational cost, reaffirming the practicality of the proposed designs for edge-oriented industrial sensing.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/LUC24141" target="_blank" >LUC24141: Simultánní komunikace a snímání pro zvyšování robustnosti systémů 6G</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

December 2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

218044-218054

Kód UT WoS článku

001652569200012

EID výsledku v databázi Scopus

2-s2.0-105025964847