Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Anomaly Detection and Fault Diagnosis in Sensor Networks
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

Anomaly Detection and Fault Diagnosis in Sensor Networks

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 4065

Special Issue Editors

Dr. Gabriele Patrizi

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139, Florence, Italy
Interests: life cycle reliability; condition monitoring for fault diagnosis of electronics; data-driven prognostic and health management; instrumentation and measurement for reliability analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Carratù

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: instrument; measurement; signal processing; sensors; sensor fusion; embeeded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor networks play a crucial role in monitoring and collecting data across various applications, from industrial processes to environmental monitoring. Ensuring the reliability and efficiency of sensor networks is crucial, as anomalies or faults can lead to erroneous data, system failures, or compromised performances. At the same time, sensor networks can, in turn, become tools for the efficient and effective detection of anomalies in other industrial systems.

Innovative methodologies leverage advanced machine learning algorithms, statistical analysis and distributed computing to identify abnormal patterns and diagnose faults in both real-time and off-line applications. The focus is on developing robust models capable of adapting to dynamic environmental conditions, varying network topologies and diverse sensor types.

This Special Issue aims to collect high-quality research papers and review articles focusing on recent advances, technologies, solutions, applications and new challenges in the field of anomaly detection and fault diagnosis techniques tailored for sensor networks.

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

  • Anomaly detection algorithms in sensor networks;
  • Fault diagnosis in sensor networks;
  • Sensor networks as a tool for anomaly detection and fault diagnosis;
  • Machine learning algorithms for anomaly detection in sensor networks;
  • Artificial intelligence methods for improving accuracy in anomaly detection;
  • Bayesian methods, statistical analysis and machine learning for identifying abnormal patterns in sensor data;
  • Distributed algorithms and protocols for real-time anomaly detection in large-scale sensor networks;
  • Adaptive fault diagnosis;
  • Energy-efficient anomaly detection and fault diagnosis in wireless sensor networks;
  • Uncertainty evaluation in anomaly detection algorithms and models;
  • Diagnostic and prognostic models using sensor networks;
  • Fault-tolerant sensor networks;
  • In-sensor machine learning computing;
  • Security aspects in anomaly detection.

Dr. Gabriele Patrizi
Dr. Marco Carratù
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

  • anomaly detection
  • fault diagnosis
  • sensor networks
  • artificial intelligence
  • machine learning
  • instrument fault detection
  • statistical analysis and Bayesian methods

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 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

21 pages, 9373 KiB  
Article
Multi-Source Information-Based Bearing Fault Diagnosis Using Multi-Branch Selective Fusion Deep Residual Network
by Shoucong Xiong, Leping Zhang, Yingxin Yang, Hongdi Zhou and Leilei Zhang
Sensors 2024, 24(20), 6581; https://doi.org/10.3390/s24206581 (registering DOI) - 12 Oct 2024
Viewed by 332
Abstract
Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, [...] Read more.
Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model. Full article
(This article belongs to the Special Issue Anomaly Detection and Fault Diagnosis in Sensor Networks)
►▼ Show Figures

Figure 1

15 pages, 4870 KiB  
Article
Anomaly Detection for Power Quality Analysis Using Smart Metering Systems
by Gabriele Patrizi, Cristian Garzon Alfonso, Leandro Calandroni, Alessandro Bartolini, Carlos Iturrino Garcia, Libero Paolucci, Francesco Grasso and Lorenzo Ciani
Sensors 2024, 24(17), 5807; https://doi.org/10.3390/s24175807 - 6 Sep 2024
Viewed by 714
Abstract
The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the [...] Read more.
The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system’s availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit. If the proposed anomaly detection algorithm detects an anomaly, further investigations using suitable classification algorithms are required. The main advantage of the proposed solution is the ability to rapidly and efficiently detect different types of anomalies with low computational complexity, allowing the implementation of the algorithm directly on the sensor node used for signal acquisition. A suitable experimental platform has been established to evaluate the advantages of the proposed method. All the different models were tested using a consistent set of hyperparameters and an output dataset generated from the principal component analysis technique. The best results achieved included models reaching 100% recall and a 92% F1 score. Full article
(This article belongs to the Special Issue Anomaly Detection and Fault Diagnosis in Sensor Networks)
►▼ Show Figures

Figure 1

27 pages, 8384 KiB  
Article
Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior
by Efe Savran, Esin Karpat and Fatih Karpat
Sensors 2024, 24(17), 5628; https://doi.org/10.3390/s24175628 - 30 Aug 2024
Cited by 1 | Viewed by 574
Abstract
Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The [...] Read more.
Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov–Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data. Full article
(This article belongs to the Special Issue Anomaly Detection and Fault Diagnosis in Sensor Networks)
►▼ Show Figures

Figure 1

14 pages, 488 KiB  
Article
Sensor Network Attack Synthesis against Fault Diagnosis of Discrete Event Systems
by Tenglong Kang, Yifan Hou and Ding Liu
Sensors 2024, 24(14), 4445; https://doi.org/10.3390/s24144445 - 9 Jul 2024
Viewed by 610
Abstract
This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We [...] Read more.
This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists. Full article
(This article belongs to the Special Issue Anomaly Detection and Fault Diagnosis in Sensor Networks)
►▼ Show Figures

Figure 1

13 pages, 6320 KiB  
Article
Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images
by Yang Xie, Yali Nie, Jan Lundgren, Mingliang Yang, Yuxuan Zhang and Zhenbo Chen
Sensors 2024, 24(11), 3428; https://doi.org/10.3390/s24113428 - 26 May 2024
Cited by 1 | Viewed by 1335
Abstract
The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder [...] Read more.
The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings. Full article
(This article belongs to the Special Issue Anomaly Detection and Fault Diagnosis in Sensor Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop