XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0

Tomasz Zyzak; Piotr Gaj; Ireneusz Smolka

doi:10.1109/BigData66926.2025.11402108

XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0

Tomasz Zyzak
, Piotr Gaj
, Ireneusz Smolka

Department of Distributed Systems and Informatic Devices (RAU/RAU8)

Kontakt Simon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sensors are key components of any control system. Their reliability determines both the correct operation of the system and the effective implementation of control algorithms. Equally important is the ability to respond rapidly to irregularities and potential failures. The use of artificial intelligence not only enables faster anomaly detection but also allows for more frequent self-analysis of devices based on data that is already being transmitted to the system. The proposed approach focuses on exploring the potential of Explainable Artificial Intelligence (XAI) to monitor sensor performance and to detect deviations from normal operation. It also investigates the possibility of utilizing the acquired information to improve control processes.

Original language	English
Title of host publication	Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025
Editors	Cheng-Zhong Xu, Leong Hou U, Xueqi Cheng, Jing Gao, Giuseppe Polese, Hong Mei, Paul Boniol, Michiaki Tatsubori, Chen Zhao, Dawei Zhou, Xiaohua Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4091-4098
Number of pages	8
Edition	2025
ISBN (Electronic)	9798331594473
DOIs	https://doi.org/10.1109/BigData66926.2025.11402108
Publication status	Published - 2025
Event	2025 IEEE International Conference on Big Data, BigData 2025 - Macau, China Duration: 8 Dec 2025 → 11 Dec 2025

Conference

Conference	2025 IEEE International Conference on Big Data, BigData 2025
Country/Territory	China
City	Macau
Period	8/12/25 → 11/12/25

Keywords

Artificial Intelligence
Industry 4.0
Machine Learning
Sensors
XAI

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData66926.2025.11402108

Cite this

Zyzak, T., Gaj, P., & Smolka, I. (2025). XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0. In C.-Z. Xu, L. H. U, X. Cheng, J. Gao, G. Polese, H. Mei, P. Boniol, M. Tatsubori, C. Zhao, D. Zhou, & X. Hu (Eds.), Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025 (2025 ed., pp. 4091-4098). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData66926.2025.11402108

Zyzak, Tomasz ; Gaj, Piotr ; Smolka, Ireneusz. / XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0. Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. editor / Cheng-Zhong Xu ; Leong Hou U ; Xueqi Cheng ; Jing Gao ; Giuseppe Polese ; Hong Mei ; Paul Boniol ; Michiaki Tatsubori ; Chen Zhao ; Dawei Zhou ; Xiaohua Hu. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 4091-4098

@inproceedings{d385814e460c4b518dd13875e48ca229,

title = "XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0",

abstract = "Sensors are key components of any control system. Their reliability determines both the correct operation of the system and the effective implementation of control algorithms. Equally important is the ability to respond rapidly to irregularities and potential failures. The use of artificial intelligence not only enables faster anomaly detection but also allows for more frequent self-analysis of devices based on data that is already being transmitted to the system. The proposed approach focuses on exploring the potential of Explainable Artificial Intelligence (XAI) to monitor sensor performance and to detect deviations from normal operation. It also investigates the possibility of utilizing the acquired information to improve control processes.",

keywords = "Artificial Intelligence, Industry 4.0, Machine Learning, Sensors, XAI",

author = "Tomasz Zyzak and Piotr Gaj and Ireneusz Smolka",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Big Data, BigData 2025 ; Conference date: 08-12-2025 Through 11-12-2025",

year = "2025",

doi = "10.1109/BigData66926.2025.11402108",

language = "English",

pages = "4091--4098",

editor = "Cheng-Zhong Xu and U, \{Leong Hou\} and Xueqi Cheng and Jing Gao and Giuseppe Polese and Hong Mei and Paul Boniol and Michiaki Tatsubori and Chen Zhao and Dawei Zhou and Xiaohua Hu",

booktitle = "Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

edition = "2025",

}

Zyzak, T, Gaj, P & Smolka, I 2025, XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0. in C-Z Xu, LH U, X Cheng, J Gao, G Polese, H Mei, P Boniol, M Tatsubori, C Zhao, D Zhou & X Hu (eds), Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. 2025 edn, Institute of Electrical and Electronics Engineers Inc., pp. 4091-4098, 2025 IEEE International Conference on Big Data, BigData 2025, Macau, China, 8/12/25. https://doi.org/10.1109/BigData66926.2025.11402108

XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0. / Zyzak, Tomasz; Gaj, Piotr ; Smolka, Ireneusz.
Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. ed. / Cheng-Zhong Xu; Leong Hou U; Xueqi Cheng; Jing Gao; Giuseppe Polese; Hong Mei; Paul Boniol; Michiaki Tatsubori; Chen Zhao; Dawei Zhou; Xiaohua Hu. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. p. 4091-4098.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0

AU - Zyzak, Tomasz

AU - Gaj, Piotr

AU - Smolka, Ireneusz

PY - 2025

Y1 - 2025

N2 - Sensors are key components of any control system. Their reliability determines both the correct operation of the system and the effective implementation of control algorithms. Equally important is the ability to respond rapidly to irregularities and potential failures. The use of artificial intelligence not only enables faster anomaly detection but also allows for more frequent self-analysis of devices based on data that is already being transmitted to the system. The proposed approach focuses on exploring the potential of Explainable Artificial Intelligence (XAI) to monitor sensor performance and to detect deviations from normal operation. It also investigates the possibility of utilizing the acquired information to improve control processes.

AB - Sensors are key components of any control system. Their reliability determines both the correct operation of the system and the effective implementation of control algorithms. Equally important is the ability to respond rapidly to irregularities and potential failures. The use of artificial intelligence not only enables faster anomaly detection but also allows for more frequent self-analysis of devices based on data that is already being transmitted to the system. The proposed approach focuses on exploring the potential of Explainable Artificial Intelligence (XAI) to monitor sensor performance and to detect deviations from normal operation. It also investigates the possibility of utilizing the acquired information to improve control processes.

KW - Artificial Intelligence

KW - Industry 4.0

KW - Machine Learning

KW - Sensors

KW - XAI

UR - https://www.scopus.com/pages/publications/105037801635

U2 - 10.1109/BigData66926.2025.11402108

DO - 10.1109/BigData66926.2025.11402108

M3 - Conference contribution

AN - SCOPUS:105037801635

SP - 4091

EP - 4098

BT - Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025

A2 - Xu, Cheng-Zhong

A2 - U, Leong Hou

A2 - Cheng, Xueqi

A2 - Gao, Jing

A2 - Polese, Giuseppe

A2 - Mei, Hong

A2 - Boniol, Paul

A2 - Tatsubori, Michiaki

A2 - Zhao, Chen

A2 - Zhou, Dawei

A2 - Hu, Xiaohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE International Conference on Big Data, BigData 2025

Y2 - 8 December 2025 through 11 December 2025

ER -

Zyzak T, Gaj P , Smolka I. XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0. In Xu CZ, U LH, Cheng X, Gao J, Polese G, Mei H, Boniol P, Tatsubori M, Zhao C, Zhou D, Hu X, editors, Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. 2025 ed. Institute of Electrical and Electronics Engineers Inc. 2025. p. 4091-4098 doi: 10.1109/BigData66926.2025.11402108

XAI-Enhanced Sensor Fusion for Reliable Predictive Analytics in Industry 4.0

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this