A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

verfasst von: Tantao Lin, Zhijun Ren, Linbo Zhu, Yongsheng Zhu, Ke Feng, Weiping Ding, Ke Yan, Michael Beer
Abstract: In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Xi'an Jiaotong University
Nantong University
City University of Macau (CityU)
The University of Liverpool
Tongji University
Typ: Übersichtsarbeit
Journal: IEEE Transactions on Instrumentation and Measurement
ISSN: 0018-9456
Publikationsdatum: 2025
Publikationsstatus: Elektronisch veröffentlicht (E-Pub)
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Instrumentierung, Elektrotechnik und Elektronik
Elektronische Version(en): https://doi.org/10.1109/TIM.2025.3529577 (Zugang: Geschlossen)

BibTeX

@article{097c1625574e4d728d5317a4b1e97a42,
title = "A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis",
abstract = "In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration.",
keywords = "Equipment Fault Diagnosis, Fusion Level, Fusion Operation, Fusion Strategy, Information Fusion, Multi-Sensor, Signal Type",
author = "Tantao Lin and Zhijun Ren and Linbo Zhu and Yongsheng Zhu and Ke Feng and Weiping Ding and Ke Yan and Michael Beer",
note = "Publisher Copyright: {\textcopyright} 1963-2012 IEEE.",
year = "2025",
doi = "10.1109/TIM.2025.3529577",
language = "English",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}