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Sensors for Fault Detection and Diagnosis
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Sensors for Fault Detection and Diagnosis

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 13729

Special Issue Editor

Prof. Dr. Giulio D'Emilia

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and of Economics, University of L’Aquila, 67100 L’Aquila, Italy
Interests: measuring systems; MEMS accelerometers; sensor integration; uncertainty assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Fault detection and diagnosis is a relevant theme in the field of methods and applications aiming at realizing condition monitoring procedures in many industrial assets. Predictive maintenance tools and device prognostics could be strongly improved if they were based on reliable fault detection and diagnosis. Vibration measurement is the most common, but other quantities or a fusion of them could also be used. A large debate is now active on these topics, in order to realize solutions able to solve problems in a generalized way, without being limited to a specific application. Furthermore, methods able to identify the occurrence of faults in the very initial phase are strongly required. Finally, robust methods for practical applications are strongly appreciated.

In order to pursue this goal in a practical and efficacious way, many aspects have to be considered, with reference to many elements:

  • Sensor (type, quantity to be measured, positioning and fusion, networking, internal or external to the controller);
  • Data processing techniques with reference to the sensors to be used (filtering, feature extraction and selection, feature ranking, methods for the classification of defects and/or parameter fitting, innovative methods for the classification and diagnosis of defects, etc.);
  • Interaction of data-driven and modeling contributions to realize hybrid approaches;
  • Uncertainty assessment of measured parameters and uncertainty analysis of processing methods;
  • Reliability assessment of methods;
  • Methods for prognostics of devices;
  • Edge and/or cloud computing;
  • Local monitoring or centralized monitoring;
  • Data recovery from the market;
  • Environmental disturbances.

More topics will also be considered if they are coherent with this theme.

Prof. Dr. Giulio D'Emilia
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

  • sensors
  • uncertainty assessment
  • fault detection
  • diagnosis
  • condition monitoring
  • prognostics
  • classification
  • machine learning
  • deep learning
  • sensor fusion
  • hybrid approach

Published Papers (4 papers)

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Research

13 pages, 3941 KiB  
Article
Uncertainty Evaluation in Vision-Based Techniques for the Surface Analysis of Composite Material Components
by Giulio D’Emilia, Antonella Gaspari, Emanuela Natale and Davide Ubaldi
Sensors 2021, 21(14), 4875; https://doi.org/10.3390/s21144875 - 17 Jul 2021
Cited by 8 | Viewed by 1715
Abstract
In this paper, a methodology is discussed concerning the measurement of yarn’s angle of two different glass-reinforced polypropylene matrix materials, widely used in the production of automotive components. The measurement method is based on a vision system and image processing techniques for edge [...] Read more.
In this paper, a methodology is discussed concerning the measurement of yarn’s angle of two different glass-reinforced polypropylene matrix materials, widely used in the production of automotive components. The measurement method is based on a vision system and image processing techniques for edge detection. Measurements of angles enable, if accurate, both useful suggestions for process optimization to be made, and the reliable validation of the simulation results of the thermoplastic process. Therefore, uncertainty evaluation of angle measurement is a mandatory pre-requisite. If the image acquisition and processing is considered, many aspects influence the whole accuracy of the method; the most important have been identified and their effects evaluated with reference to two different materials, which present different optical-type characteristics. The influence of piece geometry has also been taken into account, carrying out measurements on flat sheets and on a semi-spherical object, which is a reference standard shape, to verify the effect of thermoforming and to tune the process parameters. Complete uncertainty in the order of a few degrees has been obtained, which is satisfactory for purposes of simulation validation and consequent process optimization. The uncertainty budget also allowed individuation of the most relevant causes of uncertainty for measurement process improvement. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Diagnosis)
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17 pages, 2862 KiB  
Article
Deep Learning Approach for Vibration Signals Applications
by Han-Yun Chen and Ching-Hung Lee
Sensors 2021, 21(11), 3929; https://doi.org/10.3390/s21113929 - 07 Jun 2021
Cited by 25 | Viewed by 5774
Abstract
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types [...] Read more.
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Diagnosis)
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16 pages, 5095 KiB  
Article
On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance
by Jordi-Roger Riba, Álvaro Gómez-Pau, Jimmy Martínez and Manuel Moreno-Eguilaz
Sensors 2021, 21(11), 3739; https://doi.org/10.3390/s21113739 - 27 May 2021
Cited by 7 | Viewed by 2445
Abstract
Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest [...] Read more.
Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Diagnosis)
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29 pages, 12238 KiB  
Article
Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA
by Jose R. Huerta-Rosales, David Granados-Lieberman, Arturo Garcia-Perez, David Camarena-Martinez, Juan P. Amezquita-Sanchez and Martin Valtierra-Rodriguez
Sensors 2021, 21(11), 3598; https://doi.org/10.3390/s21113598 - 21 May 2021
Cited by 23 | Viewed by 2773
Abstract
One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. [...] Read more.
One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Diagnosis)
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