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Feature Review Papers in Fault Diagnosis & Sensors

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

Deadline for manuscript submissions: 30 January 2026 | Viewed by 834

Special Issue Editors


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Guest Editor
Department of Engineering, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: condition monitoring; structural health monitoring; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a dedicated collection of comprehensive review papers that aims to bring together the latest advancements and insights in fault diagnosis for condition monitoring, structural health monitoring, non-destructive testing and sensor technologies.

Potential topics include, but not limited to, the following:

  • Fault detection and diagnosis;
  • Fault/failure prognosis;
  • Structural health monitoring;
  • Non-destructive testing (NDT);
  • Condition monitoring;
  • Digital twins for fault diagnosis;
  • Artificial intelligence for fault diagnosis.

To be a significant resource for the scientific community, this Special Issue provides a platform for the dissemination of the current state of the art and novel perspectives in the field. We look forward to receiving your contributions and making this a successful and impactful Special Issue.

Prof. Dr. Len Gelman
Prof. Dr. Gilbert-Rainer Gillich
Prof. Dr. Shuncong Zhong
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

  • condition monitoring
  • structural health monitoring
  • non-destructive testing

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Published Papers (1 paper)

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Review

28 pages, 3303 KiB  
Review
Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review
by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian and Qi He
Sensors 2025, 25(13), 3851; https://doi.org/10.3390/s25133851 - 20 Jun 2025
Viewed by 428
Abstract
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing [...] Read more.
Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article
(This article belongs to the Special Issue Feature Review Papers in Fault Diagnosis & Sensors)
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