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Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 December 2024) | Viewed by 1663

Special Issue Editors

Dr. Fengtao Wang

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Department of Mechanical Engineering, College of Engineering, Shantou University, Daxue Road 243, Jinping District, Shantou 515063, China
Interests: additive manufacturing process monitoring and control; intelligent fault diagnosis and useful life prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Simeone

E-Mail Website
Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Interests: sensor monitoring; sustainable manufacturing; machine learning; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Weilin Ye

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, College of Engineering, Shantou University, 243 Daxue Road, Shantou 515063, China
Interests: laser spectroscopy technology; trace gas detection; infrared sensing system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, along with the rapid evolution of intelligence and informatization, sensors have begun to play an important role in industrial development. Multi-sensor information fusion is the integration of multiple sensor systems, which can collect sensor signals that characterize the state of mechanical equipment and diagnose or predict different target states through advanced signal analysis methods. Additionally, manufacturing-process monitoring and control based on multi-sensor information fusion have become research hotspots in academia and industry to ensure the reliability of component quality and the repeatability of manufacturing.

This Special Issue therefore aims to put together original research and review articles regarding recent advancements, technologies, solutions, applications, and new challenges in the field of multi-sensor information fusion.

Potential topics include but are not limited to the following:

  • Multi-sensor information fusion;
  • Advanced signal analysis and machine learning methods;
  • Intelligent fault diagnosis based on deep learning and digital twins;
  • Manufacturing process monitoring and control;
  • Data-driven smart manufacturing.

Dr. Fengtao Wang
Dr. Alessandro Simeone
Dr. Weilin Ye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-sensor information fusion
  • signal analysis
  • fault diagnosis

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Related Special Issue

Published Papers (1 paper)

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Research

12 pages, 2793 KiB  
Article
Effective Noise Reduction in NDR Systems: A Simple Yet Powerful Apriori-Based Approach
by Sajad Homayoun, Magnea Haraldsdóttir, Emil Lynge and Christian D. Jensen
Sensors 2024, 24(20), 6547; https://doi.org/10.3390/s24206547 - 11 Oct 2024
Viewed by 1167
Abstract
Noise (un-important) alerts are generally considered a major challenge in intrusion detection systems/sensors because they require more analysts to review and may cause disruption to systems that are shut down to avoid the consequences of a compromise. However, in real-world situations, many alerts [...] Read more.
Noise (un-important) alerts are generally considered a major challenge in intrusion detection systems/sensors because they require more analysts to review and may cause disruption to systems that are shut down to avoid the consequences of a compromise. However, in real-world situations, many alerts could be raised for automatic tasks being completed by some software or regular tasks by users doing their daily job. This paper proposes an approach to reduce the number of noise alerts, assuming that frequent long-term security alerts can be considered noise if their frequency is meeting some criteria, such as the minimum occurrence ratio. We prove that to effectively reduce the level of noise alerts in Network Detection and Response (NDR) systems, we are able to use simpler algorithms; sometimes, the answer is in simpler solutions, and not always in complex solutions. We study data from a real customer of a Danish NDR solution and propose an Apriori-based approach to find frequent noisy alerts. Our comparison of the detected noise before and after applying our solution shows high performance in reducing noise alerts for most of the alert types for a real customer. Our experiments show that our method can filter more than 40% of the alerts by setting the minimum occurrences to 70%. Moreover, our results show that we were able to filter out more than 90% for some alert categories. Full article
(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis—2nd Edition)
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