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Information
Special Issues
Fault Diagnosis, Maintenance and Reliability
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Fault Diagnosis, Maintenance and Reliability

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (1 May 2019) | Viewed by 16570

Special Issue Editors

Prof. Dr. Adam Glowacz

E-Mail Website
Guest Editor
Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: machine; fault diagnosis; pattern recognition; IoT; signal processing; signal analysis; image processing; computer science; automatic
Special Issues, Collections and Topics in MDPI journals
Dr. Jerzy Józwik

E-Mail Website1 Website2
Guest Editor
Faculty of Mechanical Engineering, Department of Production Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Interests: cutting processes; surface metrology; tribology; maintenance; process diagnostics; additive manufacturing technologies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Grzegorz Królczyk

E-Mail Website
Guest Editor
Department of Manufacturing Engineering and Production Automation, Opole University of Technology, 45-758 Opole, Poland
Interests: fault diagnosis; vibration analysis; measurement; materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in fault diagnosis, maintenance and reliability.

The scope of this Special Issue encompasses applications in engineering, mechanical engineering, electrical engineering, instrumentation, thermal imaging, measurement, acoustics.

Dr. Adam Glowacz
Prof. Grzegorz Krolczyk
Dr. Jerzy Jozwik
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • fault diagnosis
  • machine
  • reliability
  • maintenance
  • signal processing
  • measurement

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Published Papers (3 papers)

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Research

27 pages, 7051 KiB  
Article
Machine Vibration Monitoring for Diagnostics through Hypothesis Testing
by Alessandro Paolo Daga and Luigi Garibaldi
Information 2019, 10(6), 204; https://doi.org/10.3390/info10060204 - 7 Jun 2019
Cited by 24 | Viewed by 5408
Abstract
Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety [...] Read more.
Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes. Full article
(This article belongs to the Special Issue Fault Diagnosis, Maintenance and Reliability)
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13 pages, 2280 KiB  
Article
A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion
by Tianzhen Wang, Jingjing Dong, Tao Xie, Demba Diallo and Mohamed Benbouzid
Information 2019, 10(3), 116; https://doi.org/10.3390/info10030116 - 17 Mar 2019
Cited by 15 | Viewed by 4287
Abstract
This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if [...] Read more.
This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%. Full article
(This article belongs to the Special Issue Fault Diagnosis, Maintenance and Reliability)
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20 pages, 1432 KiB  
Article
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers
by Joao Ferreira, Gustavo Callou, Albert Josua, Dietmar Tutsch and Paulo Maciel
Information 2019, 10(3), 113; https://doi.org/10.3390/info10030113 - 14 Mar 2019
Cited by 18 | Viewed by 5770
Abstract
Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption [...] Read more.
Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. Full article
(This article belongs to the Special Issue Fault Diagnosis, Maintenance and Reliability)
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