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Special Issue "Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring"

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

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Rafal Burdzik
Website
Guest Editor
Faculty of Transport, Silesian University of Technology, Katowice, Poland
Interests: signal processing; measurement and control; noise and vibration; transport
Prof. Dr. Minvydas Ragulskis
Website
Guest Editor
Department of Mathematical Modeling, Kaunas University of Technology, Lithuania
Department of Information Science, National Taiwan University of Education, Taiwan
Interests: development of numerical methods; modelling of optical effects; visual cryptography; nonlinear dynamical systems and chaos
Special Issues and Collections in MDPI journals
Dr. Maosen Cao
Website
Guest Editor
1. Jiangxi Provincial Key Laboratory of Environmental Geotechnical Engineering and Disaster Control, Jiangxi University of Science and Technology, Ganzhou, 341000, People’s Republic of China; 2. College of Mechanics and Materials, Hohai University, Nanjing 210098, People’s Republic of China
Interests: structural durability and integrity assessment; health monitoring; damage modeling and identification; disaster prevention and mitigation; multiscale vibration and dynamics
Dr. Radosław Zimroz
Website
Guest Editor
Wrocław University of Science and Technology, Wrocław, Poland
Interests: data analysis, engineering; applied and computational mathematics; dynamic analysis; mechanical engineering; mechatronics; vibration analysis; signal analysis
Dr. Chaari Fakher
Website
Guest Editor
National School of Engineers of Sfax, Sfax, Tunisia
Interests: machine and structure dynamics; vibro-acoustic behavior of machines and structures
Dr. Łukasz Konieczny
Website
Guest Editor
Silesian University of Technology, Katowice, Poland
Interests: structural analysis; finite element modeling; structural dynamics; automobile engineering; nonlinear analysis; dynamic analysis

Special Issue Information

Dear Colleagues,

I would like to interest you in this Special Issue: Sensing Technology and Data Interpretation in Machine Diagnosis and Systems at Sensors and cordially invite you to submit your articles. The purpose of this Special Issue is to compile studies on knowledge, research practice, and forecast development trends in the field of machine and system diagnostics, with particular emphasis on measuring systems and signal processing methods to extract useful information. The dynamic development of the Smart concept in all engineering areas indicates the need for consolidation and exchange of knowledge in this area, for which this Special Issue is an ideal platform. Hence, in terms of engineering applications and research problems, we are not limiting the articles that can be submitted to this Special Issue.

Machine diagnosis and systems condition monitoring are fundamental processes for decision making protocols in all mechanical systems. Control and steering of all systems determine its operational and functional activities. These decisions must be made based on proper data. Therefore, the most appropriate data must first be obtained and then the important information components must be separated. All components of the logical decision path that may degrade the quality of the data acquired or reduce operational reliability must be avoided and eliminated. Therefore, it is important to correctly indicate the data acquisition points, select the most suitable sensors, correctly complete the entire measurement path and dedicated signal analysis.

This Special Issue will focus on recent attempts in development of sensors and sensing technology due to novel possibilities in machine diagnosis and systems condition monitoring to underline this new knowledge and application, especially for the trend in smart machines and systems with self-diagnosis properties. An example of this is the concept of smart cities and intelligent systems, which is trending worldwide. The huge demand for continuous comprehensive information in relation to all areas of the city's functioning, especially transport, is a considerable challenge. This Special Issue will collect interdisciplinary approaches on sensors and sensing technology in machine diagnosis and systems condition monitoring, including the consideration and development of some innovative directions in research.

The potential scope includes but is not limited to the following:

  • Methods and apparatuses for machine diagnosis and systems condition monitoring;
  • Signal processing, data fusion, and deep learning in sensor systems;
  • Damage detection and identification in machines;
  • Condition monitoring in systems;
  • Sensors in control and steering of the system;
  • 5G/6G technologies;
  • Identification of machinery non-stationary and anomalous operation;
  • Advanced signal processing methods for machine diagnosis and condition monitoring;
  • Practical cases of machine diagnosis and systems condition monitoring;
  • Machine and system assessment under noisy conditions;
  • Intelligent transport systems;
  • Sensor network and relationships;
  • Smart/intelligent sensors;
  • Sensor technology and application for machine diagnosis and systems condition monitoring;
  • Internet of Things for machine diagnosis and systems condition monitoring;
  • Localization and object tracking in smart cities;
  • Machine learning applications;
  • Complex machine and system analysis using multiple sensors;
  • Techniques for online, real-time system condition monitoring.

Prof. Dr. Rafal Burdzik
Dr. Minvydas Ragulskis
Dr. Maosen Cao
Dr. Radosław Zimroz
Dr. Chaari Fakher
Dr. Łukasz Konieczny
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 papers will be 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 2000 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

  • machine diagnosis
  • systems condition monitoring
  • sensors
  • sensing technology
  • smart city
  • IoT

Published Papers (5 papers)

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Open AccessArticle
Automated Calibration System for Digital Multimeters Not Equipped with a Communication Interface
Sensors 2020, 20(13), 3650; https://doi.org/10.3390/s20133650 - 29 Jun 2020
Abstract
This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical [...] Read more.
This article is focused on the calibration of digital multimeters, in which the concept and practical solutions for stations with software for automatic calibration are presented. This paper also presents the general structure of the measuring system, the application scheme, and the technical implementation of measuring stations, together with the software. Full article
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Open AccessArticle
Research on the Single-Value Indicators for Centrifugal Pump Based on Vibration Signals
Sensors 2020, 20(11), 3283; https://doi.org/10.3390/s20113283 - 09 Jun 2020
Abstract
Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value [...] Read more.
Off-design operation conditions might not only seriously affect the internal flow status of a centrifugal pump, but also result in additional energy loss and potential mechanical damage. Therefore, early-stage monitoring and predication on off-design operation conditions for centrifugal pumps have become essential. Single-value indicators have favorable factors such as a smaller amount of calculation and easier identification. As a result, industries prefer the more straightforward approach: obtaining single-value indicators directly from the signals which could be easier compared with accepted standards. The possibility of applying the single-value indicators of vibration into operation condition monitoring for a centrifugal pump is studied theoretically and experimentally, which shows that the statistical features of vibration might be suitable for hydraulic instability detection for a centrifugal pump. Full article
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Open AccessArticle
A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments
Sensors 2020, 20(7), 2115; https://doi.org/10.3390/s20072115 - 09 Apr 2020
Abstract
Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the [...] Read more.
Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components. Full article
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Open AccessArticle
Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings
Sensors 2020, 20(7), 1845; https://doi.org/10.3390/s20071845 - 26 Mar 2020
Cited by 1
Abstract
Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in [...] Read more.
Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets. Full article
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Other

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Open AccessLetter
Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors
Sensors 2020, 20(12), 3436; https://doi.org/10.3390/s20123436 - 18 Jun 2020
Abstract
As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation [...] Read more.
As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%. Full article
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