Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2574

Special Issue Editor

Dr. Md Junayed Hasan

E-Mail Website
Guest Editor
National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK
Interests: digital condition monitoring; mechanical signal processing; computer vision, machine learning; multimodal data fusion; artificial intelligence; digital fault inspection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the industrial sector, machinery and mechanical structures are susceptible to deterioration and performance decline over time. Consequently, the collection and processing of data from a variety of sensors has become crucial for the timely diagnosis of deterioration symptoms and the accurate prediction of future health conditions. Using artificial intelligence (AI) technology, models are being developed based on historical sensor data which have enormous potential for fault diagnosis and prognosis in industrial equipment. As the deployment of Internet of Things (IoT) and cloud-based technologies for stateful maintenance increases in the future, AI-powered solutions will become even more crucial for managing the vast quantities of available measurement data for decision making.

This Special Issue aims to investigate fault diagnosis and prognosis of industrial equipment and mechanical structures by utilising a variety of sensors, including those related to image, video, and multimodal information fusion. We welcome researchers to submit articles discussing sensor-based artificial neural network technology, multimodal data fusion, explainable AI solutions, and objects for error diagnosis and prognosis in the context of Industry 4.0, cloud computing, cyber–physical systems, and machine-to-machine interfaces and paradigms.

Dr. Md Junayed Hasan
Guest Editor

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

  • multimodal data fusion
  • fault diagnosis
  • digital condition monitoring
  • predictive maintenance
  • artificial intelligence
  • net-zero challenges with AI

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 7702 KiB  
Article
Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold
by Baojun Yang, Wei Liu, Sheng Lu and Jiufei Luo
Sensors 2024, 24(5), 1380; https://doi.org/10.3390/s24051380 - 21 Feb 2024
Viewed by 387
Abstract
Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but [...] Read more.
Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)
►▼ Show Figures

Figure 1

30 pages, 3401 KiB  
Article
Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors
by Yanyang Li, Jindong Wang, Haiyang Zhao, Chang Wang and Qi Shao
Sensors 2024, 24(1), 167; https://doi.org/10.3390/s24010167 - 27 Dec 2023
Viewed by 710
Abstract
Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the [...] Read more.
Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)
►▼ Show Figures

Figure 1

18 pages, 6557 KiB  
Article
Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5
by Shengsuo Niu, Xiaosen Zhou, Dasen Zhou, Zhiyao Yang, Haiping Liang and Haifeng Su
Sensors 2023, 23(14), 6410; https://doi.org/10.3390/s23146410 - 14 Jul 2023
Cited by 4 | Viewed by 1104
Abstract
Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a [...] Read more.
Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a “morphological experiment”, while an “algorithm comparison experiment” confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop