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Vibration Sensor-Based Diagnosis and Prognosis Technologies and Systems: Part II
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Vibration Sensor-Based Diagnosis and Prognosis Technologies and Systems: Part II

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 5036

Special Issue Editor

Prof. Dr. Len Gelman

E-Mail Website
Guest Editor
School of Computing and Engineering, Department of Engineering and Technology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: digital signal processing; structural health monitoring; condition monitoring; artificial intelligence; vibration analysis; motor current signature analysis; adaptation of diagnosis systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vibration sensor-based diagnosis and prognosis technologies/systems have become very important for most industrial sectors and academic research, the most challenging topic in this field being the vibration sensor-based diagnosis and prognosis of machineries and structures.

The main challenges for these areas are as follows:

  • Most industrial assets/machineries work in non-stationary operations;
  • Most excitations of engineering structures and, therefore, sensor outputs are non-stationary;
  • One of the most important industrial requirements for vibration sensor-based diagnosis technologies is an effective diagnosis at an early stage of damage development.

Addressing these challenges requires novel developments related to vibration sensors and intelligent vibration sensors, time-frequency and the non-linear higher-order spectral analysis of sensor data and adaptation of vibration sensor-based diagnosis technologies to non-stationary conditions related to machineries and structures.

Therefore, this Special Issue focuses on vibration sensor-based diagnosis and prognosis technologies and systems for machineries/structures, paying attention mainly to novel developments related to vibration sensors and intelligent vibration sensors, signal processing of sensor data, artificial intelligence and machine learning for diagnostic decision making and the adaptation of vibration sensor-based diagnosis technologies to non-stationary conditions related to machineries and structures.

This Special Issue will not cover non-novel “case study papers”. Potential authors need to provide clear statements of paper novelties that should be based on comprehensive state-of-the art reviews.

The following keywords describe this Special Issue:

  • classical, time-frequency and higher-order signal processing;
  • artificial intelligence/machine learning;
  • vibration sensor-based structural health monitoring technologies and systems for engineering structures;
  • vibration sensor-based condition monitoring technologies and systems for machinery and complex electromechanical assets;
  • adaptive vibration sensor-based diagnosis and prognosis technologies and systems;
  • vibration sensor-based diagnosis and prognosis technologies and systems for linear and non-linear asset components/assets;
  • diagnostic and prognostic feature extraction
  • finite element modelling, modal analysis and computational fluid dynamics;
  • Internet of Things and services;
  • physics of failure and failure modes;
  • root cause analysis;
  • sensor fusion and data mining;
  • image processing;
  • big data, data analytics and smart data management

Prof. Dr. Len Gelman
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 (3 papers)

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Research

20 pages, 165826 KiB  
Article
Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
by Cihan Ates, Tobias Höfchen, Mario Witt, Rainer Koch and Hans-Jörg Bauer
Sensors 2023, 23(22), 9212; https://doi.org/10.3390/s23229212 - 16 Nov 2023
Cited by 2 | Viewed by 795
Abstract
Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital [...] Read more.
Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data. Full article
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis and Prognosis Technologies and Systems: Part II)
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15 pages, 22723 KiB  
Article
Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements
by Yang Zuo, Jan Lundberg, Taoufik Najeh, Matti Rantatalo and Johan Odelius
Sensors 2023, 23(7), 3666; https://doi.org/10.3390/s23073666 - 31 Mar 2023
Cited by 4 | Viewed by 1416
Abstract
Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing [...] Read more.
Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing downtime or even severe accidents. A squat is a common defect of S&Cs that has to be monitored and repaired to reduce such risks. In this study, a testbed including a full-scale S&C and a bogie wagon was developed. Vibrations were measured for different squat sizes by an accelerometer mounted at the point machine. A method of processing the vibration data and the speed data is proposed to investigate the possibility of detecting and quantifying the severity of a squat. One key technology used is wavelet denoising. The study shows that it is possible to monitor the development of the squat size on the rail up to around 13 m from the point machine. The relationships between the normalised peak-to-peak amplitude of the vibration signal and the squat depth were also estimated. Full article
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis and Prognosis Technologies and Systems: Part II)
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15 pages, 4135 KiB  
Article
Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest
by Yang Zuo, Florian Thiery, Praneeth Chandran, Johan Odelius and Matti Rantatalo
Sensors 2022, 22(17), 6357; https://doi.org/10.3390/s22176357 - 24 Aug 2022
Cited by 6 | Viewed by 1690
Abstract
Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and [...] Read more.
Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects. Full article
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis and Prognosis Technologies and Systems: Part II)
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