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Machines
Special Issues
Vibration Based Condition Monitoring
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Vibration Based Condition Monitoring

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 7659

Special Issue Editors

Prof. Dr. Jerome Antoni

E-Mail Website
Guest Editor
Laboratoire Vibrations Acoustique, University of Lyon, 69621 Villeurbanne, France
Interests: health monitoring; (operational) modal analysis; source identification; acoustic imaging; inverse problems; signal processing; machine learning
Dr. Zoltan-Iosif Korka

E-Mail Website
Guest Editor
Department of Engineering Sciences, Babes-Bolyai University, Traian Vuia square, No. 1 - 4, 320085 Reşiţa, Romania
Interests: damage detection; gear design; gear manufacturing; gear testing; vibration

Special Issue Information

Dear Colleagues,

Vibration-based condition monitoring as well as damage detection and identification are very important concerns related to accurate operation of machines, in order to ensure their desirable operational properties, safety, and integrity. In this respect, in the last few years an expanding increase of vibration-based condition monitoring methods for damage detection and identification in machines has been noticed. This evolution was positively influenced by following factors: the advance of measurement techniques and devices in vibration engineering, as well as the progress of mathematical tools for signal processing and conditioning. All these factors impact actual tendencies related to vibration-based condition monitoring and damage identification in machines and their components.

We are pleased to invite you to contribute on this Special Issue, because it will collect interdisciplinary contributions on vibration-based condition monitoring and damage identification, being a strong motivation to consider and develop some original aspects of research.

This Special Issue aims to cover condition monitoring of machines and damage identification topics related to numerical simulation and theoretical studies as well as practical solutions applicable in vibration-generating devices and rotating machines, structural elements of heavy machines, vehicles and so on.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Methods and apparatus of vibration-based condition monitoring
  • Advanced signal processing methods for vibration-based condition monitoring
  • Advanced theoretical discussion on vibration propagation and dissipation in technical systems
  • Damage detection and condition monitoring in rotating machines
  • Damage detection in structural elements of machines
  • Damage modelling and simulations
  • Techniques on online, real-time condition monitoring of rotating machines
  • Damage and monitoring analytics
  • Practical cases of vibration-based condition monitoring
  • Assessment of damage under noisy conditions

We look forward to receiving your contributions.

Prof. Dr. Jerome Antoni
Dr. Zoltan-Iosif Korka
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. Machines 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 2400 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

  • condition monitoring
  • damage detection
  • damage modelling
  • noise
  • rotating machine
  • signal processing
  • simulation
  • vibration

Published Papers (3 papers)

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Research

16 pages, 7257 KiB  
Article
A Study on the Vibration Characteristics and Damage Mechanism of Pantograph Strips in a Railway Electrification System
by Qirui Wu, Xiaohan Phrain Gu, Ziyan Ma and Anbin Wang
Machines 2022, 10(8), 710; https://doi.org/10.3390/machines10080710 - 18 Aug 2022
Cited by 1 | Viewed by 1875
Abstract
This paper presents the vibration characteristics of a pantograph–catenary interaction in a rigid catenary system. Both computational simulation and laboratory tests are carried out to evaluate the frequency contents of pantograph strips. Based on the observation that irregular wear is characterized by the [...] Read more.
This paper presents the vibration characteristics of a pantograph–catenary interaction in a rigid catenary system. Both computational simulation and laboratory tests are carried out to evaluate the frequency contents of pantograph strips. Based on the observation that irregular wear is characterized by the consistency between the pantograph strips’ wear pattern and the mode shape of their dominant modal frequencies, it is deducted that resonance occurs at the pantograph strip and the contact wire interface in the high frequency range. By applying damping treatment to the pantograph strip, and hence improving its damping property, a reduction of 7 dB in the total vibration level at the sliding contact can be achieved, as verified through field tests. It is also found that the worse the initial condition of the pantograph–catenary system, the more prominent the damping effects on the control of high-frequency vibration for irregular wear problems. Full article
(This article belongs to the Special Issue Vibration Based Condition Monitoring)
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15 pages, 22843 KiB  
Article
Acoustic Signal Classification Using Symmetrized Dot Pattern and Convolutional Neural Network
by Jian-Da Wu, Wen-Jun Luo and Kai-Chao Yao
Machines 2022, 10(2), 90; https://doi.org/10.3390/machines10020090 - 25 Jan 2022
Cited by 4 | Viewed by 2496
Abstract
The classification of sound signals can be applied to the fault diagnosis of mechanical systems, such as vehicles. The traditional sound classification technology mainly uses the time-frequency domain characteristics of signals as the basis for identification. This study proposes a technique for visualizing [...] Read more.
The classification of sound signals can be applied to the fault diagnosis of mechanical systems, such as vehicles. The traditional sound classification technology mainly uses the time-frequency domain characteristics of signals as the basis for identification. This study proposes a technique for visualizing sound signals, and uses artificial neural networks as the basis for signal classification. This feature extraction method mainly uses a principle to convert a time domain signal into a coordinate symmetrized dot pattern, and presents it in the form of snowflakes through signal conversion. To verify the feasibility of this method to classify different noise characteristic signals, the experimental work is divided into two parts, which are the identification of traditional engine vehicle noise and electric motor noise. In sound measurement, we first use the microphone and data acquisition system to measure the noise of different vehicles under the same operating conditions or the operating noise of different electric motors. We then convert the signal in the time domain into a symmetrized dot pattern and establish an acoustic symmetrized dot pattern database, and use a convolutional neural network to identify vehicle types. To achieve a better identification effect, in the process of data analysis, the effect of the time delay coefficient and weighting coefficient on the image identification effect is discussed. The experimental results show that the method can be effectively applied to the identification of traditional engine and electric vehicle classification, and can effectively achieve the purpose of sound signal classification. Full article
(This article belongs to the Special Issue Vibration Based Condition Monitoring)
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18 pages, 11838 KiB  
Article
The Cavitation-Induced Pressure Fluctuations in a Mixed-Flow Pump under Impeller Inflow Distortion
by Huiyan Zhang, Fan Meng, Yunhao Zheng and Yanjun Li
Machines 2021, 9(12), 326; https://doi.org/10.3390/machines9120326 - 30 Nov 2021
Cited by 3 | Viewed by 2237
Abstract
To reduce cavitation-induced pressure fluctuations in a mixed-flow pump under impeller inflow distortion, the dynamic pressure signal at different monitoring points of a mixed-flow pump with a dustpan-shaped inlet conduit under normal and critical cavitation conditions was collected using high-precision digital pressure sensors. [...] Read more.
To reduce cavitation-induced pressure fluctuations in a mixed-flow pump under impeller inflow distortion, the dynamic pressure signal at different monitoring points of a mixed-flow pump with a dustpan-shaped inlet conduit under normal and critical cavitation conditions was collected using high-precision digital pressure sensors. Firstly, the nonuniformity of the impeller inflow caused by inlet conduit shape was characterized by the time–frequency-domain spectra and statistical characteristics of pressure fluctuation at four monitoring points (P4–P7) circumferentially distributed at the outlet of the inlet conduit. Then, the cavity distribution on the blade surface was captured by a stroboscope. Lastly, the characteristics of cavitation-induced pressure fluctuation were obtained by analyzing the time–frequency-domain spectra and statistical characteristic values of dynamic pressure signals at the impeller inlet (P1), guide vanes inlet (P2), and guide vanes outlet (P3). The results show that the flow distribution of impeller inflow is asymmetric. The pav values at P4 and P6 were the smallest and largest, respectively. Compared with normal conditions, the impeller inlet pressure is lower under critical cavitation conditions, which leads to low pav, pp-p and a main frequency amplitude at P1. In addition, the cavity covered the whole suction side under H = 13.6 m and 15.5 m, which led the pp-p and dominant frequency amplitude of pressure fluctuation at P2 and P3 under critical cavitation to be higher than that under normal conditions. Full article
(This article belongs to the Special Issue Vibration Based Condition Monitoring)
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