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Trends and Applications in Sensor Fault Diagnosis

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6463

Special Issue Editors


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Guest Editor
Department of Industrial Engineering (DIIn), University of Salerno, 132 - 84084 Fisciano, Italy
Interests: fault diagnosis; motorcycles; statistical analysis; sensors; nuclear magnetic resonance; quality control; reliability; vibration control
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Guest Editor
Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Interests: RF measurements; EMC measurements; measurements for network and traffic analysis; measurements for cyber security; measurements for modern telecommunication systems; spectrum sensing for cognitive radios
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Owing to factors such as the rising demand for electronics and automobiles, increasing concern for surveillance and security, and the development of wireless technology and smart cities, the global Smart Sensor market value is estimated to reach USD 101 billion by 2025 with a CAGR of 19%.

Smart Sensors can autonomously sense, gather, judge, analyze, and process external environmental data using multi-element integrated circuits, with sensors, communication modules, microprocessors, drivers and interfaces, and software algorithms all embedded into one system-level device. In addition to the high reliability and cost performance typically exhibited by Smart Sensors, some of their most important advantages are their self-check, self-calibration, and self-diagnosis capabilities.

New diagnostic approaches and techniques are required to maximize the capability of these features to transform data into useful information for Fault-Tolerant Measurement and Control Systems.

For this Special Issue, we invite authors from academia and industry to submit new research on technological innovations and novel applications in the automatic diagnosis of sensor systems, with a special focus on real-time applications. Research topics include, but are not limited to:

  • Innovative electronic and mechatronic sensors with self-diagnosis capabilities;
  • New techniques and approaches for Instrument Fault Detection Isolation and Accommodation (IFDIA);
  • New architecture for Fault-Tolerant Control Systems;
  • Fault diagnosis in Distributed Measurement Networks;
  • The industrial applications of sensor fault diagnosis;
  • Testing algorithms and approaches for estimating sensor reliability and life-cycles.

Dr. Paolo Sommella
Dr. Domenico Capriglione
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

  • sensor fault diagnosis
  • sensor reliability
  • fault-tolerant control systems
  • reliability of distributed measurement systems
  • sensor self-diagnosis

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Published Papers (2 papers)

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Research

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19 pages, 5175 KiB  
Article
Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter
by András Darányi and János Abonyi
Sensors 2024, 24(3), 719; https://doi.org/10.3390/s24030719 - 23 Jan 2024
Cited by 1 | Viewed by 1297
Abstract
This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure [...] Read more.
This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. Full article
(This article belongs to the Special Issue Trends and Applications in Sensor Fault Diagnosis)
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Review

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17 pages, 294 KiB  
Review
Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis
by Paolo Mercorelli
Sensors 2024, 24(8), 2656; https://doi.org/10.3390/s24082656 - 22 Apr 2024
Cited by 2 | Viewed by 4662
Abstract
Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting [...] Read more.
Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data. Full article
(This article belongs to the Special Issue Trends and Applications in Sensor Fault Diagnosis)
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