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Fault Diagnosis of Modern Systems and Sensors
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Fault Diagnosis of Modern Systems and Sensors

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

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 15179

Special Issue Editor

Dr. Paolo Sommella

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cyber Physical Systems (CPSs) are integrations of computation, networking, and physical processes: the combination of several systems of different nature whose main purpose is to control a physical process and, through feedback, adapt itself to new conditions, in real time.

CPSs promise to transform industries and many sites (hospitals, ports) in innovative ways.

As an example, in the future factory robots, automated guided vehicles (AGVs), sensors, controllers, raw materials, products, and databases communicate with one another and be orchestrated through a central intelligence which monitors and controls operations at all levels. Thus, Production environments will be self-configuring, self-adjusting, and self-optimizing, leading to greater agility, flexibility, and cost effectiveness.

These requirements ask for new approaches for measurement systems and techniques in order to make effective the capability of transforming data into useful information for decision makers.

All the steps of the measurement process are involved: transducer selection and installation, sensor calibration, system modelling and its interaction with sensors, sensor fusion and networking, data acquisition and data processing methods and algorithms, measurement uncertainty modelling and management. Management of the sensor systems and validation techniques are also noteworthy in this scenario.

This Special Issue encourages authors, from academia and industry, to submit new research results about technological innovations and novel applications for the automatic diagnosis of the systems faults, with special interest to industrial applications. The Special Issue topics include, but are not limited to:

  • Innovative electronic and mechatronic sensors applied in production monitoring;
  • Smart and virtual sensors for industry application;
  • In line sensors data processing and fusion in automatic processes;
  • Instrument Fault Detection Isolation and Accommodation (IFDI);
  • Testing algorithms and approaches for estimating sensor reliability and life-cycle;
  • Diagnostic Techniques applied to CPSs.

Dr. Paolo Sommella
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.

Published Papers (4 papers)

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Research

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21 pages, 1278 KiB  
Article
A Belief Network Reasoning Framework for Fault Localization in Communication Networks
by Rongyu Liang, Feng Liu and Jie Liu
Sensors 2020, 20(23), 6950; https://doi.org/10.3390/s20236950 - 05 Dec 2020
Cited by 9 | Viewed by 1880
Abstract
A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault [...] Read more.
A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault’s root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability. Full article
(This article belongs to the Special Issue Fault Diagnosis of Modern Systems and Sensors)
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19 pages, 6392 KiB  
Article
Output-Only Damage Detection in Plate-Like Structures Based on Proportional Strain Flexibility Matrix
by Kang Yun, Mingyao Liu, Jiangtao Lv, Jingliang Wang, Zhao Li and Han Song
Sensors 2020, 20(23), 6862; https://doi.org/10.3390/s20236862 - 30 Nov 2020
Cited by 3 | Viewed by 1273
Abstract
For engineering structures, strain flexibility-based approaches have been widely used for structural health monitoring purposes with prominent advantages. However, the applicability and robustness of the method need to be further improved. In this paper, a novel damage index based on differences in uniform [...] Read more.
For engineering structures, strain flexibility-based approaches have been widely used for structural health monitoring purposes with prominent advantages. However, the applicability and robustness of the method need to be further improved. In this paper, a novel damage index based on differences in uniform load strain field (ULSF) is developed for plate-like structures. When estimating ULSF, the strain flexibility matrix (SFM) based on mass-normalized strain mode shapes (SMSs) is needed. However, the mass-normalized strain mode shapes (SMSs) are complicated and difficult to obtain when the input, i.e., the excitation, is unknown. To address this issue, the proportional strain flexibility matrix (PSFM) and its simplified construction procedure are proposed and integrated into the frames of ULSF, which can be easily obtained when the input is unknown. The identification accuracy of the method under the damage with different locations and degrees is validated by the numerical examples and experimental examples. Both the numerical and experimental results demonstrate that the proposed method provides a reliable tool for output-only damage detection of plate-like structures without estimating the mass-normalized strain mode shapes (SMSs). Full article
(This article belongs to the Special Issue Fault Diagnosis of Modern Systems and Sensors)
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Review

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19 pages, 338 KiB  
Review
Fault Handling in Industry 4.0: Definition, Process and Applications
by Heiko Webert, Tamara Döß, Lukas Kaupp and Stephan Simons
Sensors 2022, 22(6), 2205; https://doi.org/10.3390/s22062205 - 12 Mar 2022
Cited by 18 | Viewed by 3998
Abstract
The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment [...] Read more.
The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed. Full article
(This article belongs to the Special Issue Fault Diagnosis of Modern Systems and Sensors)
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26 pages, 2384 KiB  
Review
Cutting Forces Assessment in CNC Machining Processes: A Critical Review
by Vitor F. C. Sousa, Francisco J. G. Silva, José S. Fecheira, Hernâni M. Lopes, Rui Pedro Martinho, Rafaela B. Casais and Luís Pinto Ferreira
Sensors 2020, 20(16), 4536; https://doi.org/10.3390/s20164536 - 13 Aug 2020
Cited by 35 | Viewed by 7213
Abstract
Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces [...] Read more.
Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works. Full article
(This article belongs to the Special Issue Fault Diagnosis of Modern Systems and Sensors)
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