Structural Health Monitoring for Mechanical Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 10748

Special Issue Editors


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Guest Editor
School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: condition monitoring; plant maintenance

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Guest Editor
School Of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 5FB, UK
Interests: vibration; condition monitoring; rotating machines; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The health monitoring approach of mechanical structures (machines, structures, piping, etc.) is a well-accepted tool for condition-based maintenance (CBM) activity in industries. This monitoring approach generally helps to predict any developing fault(s) at an early stage so that maintenance or remedial action can be performed before any catastrophic failure. However, health monitoring approaches are changing rapidly due to recent advancements in technologies in both instruments and data analyses. Therefore, the scope of this Special Issue on Structural Health Monitoring for Mechanical Systems” will cover recent trends in health monitoring approaches.

Prof. Dr. Jyoti K. Sinha
Dr. Faris Elasha
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

  • AI technologies
  • Condition-based maintenance
  • Condition monitoring
  • Digital twin model
  • Maintenance-based on condition monitoring
  • Fault diagnosis and prognosis
  • Industry 4.0 and IoT
  • Machine health monitoring
  • Machine learning
  • Mobile technology
  • Robot-based health monitoring and diagnostics
  • Sensing and instrumentation
  • Signal and image processing methods
  • Wireless sensing and health monitoring
  • Vibro-acoustic monitoring
  • Vibration-based health monitoring

Published Papers (4 papers)

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Research

13 pages, 4590 KiB  
Article
Contactless Fault Detection of a DC Motor Direction of Rotation Using Its Stray Magnetic Field
by Michal Matějásko, Martin Brablc, Martin Appel and Robert Grepl
Machines 2021, 9(11), 281; https://doi.org/10.3390/machines9110281 - 10 Nov 2021
Viewed by 2043
Abstract
In large-scale manufacturing and assembly applications, especially when trying to automate most steps, implementing quality control as early in the process as possible is the key to prevent expenses later. We deal mainly with the production of DC motor powered fuel pumps, which [...] Read more.
In large-scale manufacturing and assembly applications, especially when trying to automate most steps, implementing quality control as early in the process as possible is the key to prevent expenses later. We deal mainly with the production of DC motor powered fuel pumps, which are commonly used in the automotive industry. The goal of this paper is to present a newly developed technique for non-invasive fault detection of a DC motor’s direction of rotation using a stray magnetic field out of the motor chassis. The results presented in this paper show that it is possible to detect faults even on low-power motors while the algorithm is kept as simple as possible to allow for large-scale deployment on a production line. It also gives new insight into the behavior of the stray magnetic field of electric motors, which may benefit other applications and future research. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Mechanical Systems)
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19 pages, 4448 KiB  
Article
Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques
by Syed Muhammad Tayyab, Steven Chatterton and Paolo Pennacchi
Machines 2021, 9(8), 173; https://doi.org/10.3390/machines9080173 - 18 Aug 2021
Cited by 13 | Viewed by 2407
Abstract
Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, [...] Read more.
Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Mechanical Systems)
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24 pages, 4504 KiB  
Article
Effects of Different Hard Finishing Processes on Gear Excitation
by Maximilian Trübswetter, Joshua Götz, Bernhard Kohn, Michael Otto and Karsten Stahl
Machines 2021, 9(8), 169; https://doi.org/10.3390/machines9080169 - 16 Aug 2021
Cited by 2 | Viewed by 2576
Abstract
Gearboxes are essential in mechanical drive trains for power transmission. A low noise emission and thus an optimized excitation behavior is a substantial design objective for many applications in terms of comfort and operational safety. There exist numerous processes for manufacturing gears, which [...] Read more.
Gearboxes are essential in mechanical drive trains for power transmission. A low noise emission and thus an optimized excitation behavior is a substantial design objective for many applications in terms of comfort and operational safety. There exist numerous processes for manufacturing gears, which all show different properties in relation to the process variables and, therefore, differences in the resulting accuracy and quality of the gear flank. In this paper, the influence of three different manufacturing processes for hard finishing—continuous generating grinding, polish grinding and gear skiving—on the surface structure of gear flanks and the excitation behavior is investigated experimentally and analyzed by the application force level. A tactile scanning of the gear flanks determines the flank surface structure and the deviations from the desired geometry. A torsional acceleration measurement during speed ramp-ups at different load levels is used to analyze the excitation of the gears. The results show only a minor influence of the surface structure on the application force level. The excitation behavior is dominated by the influence of the flank modification and its deviation from the design values. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Mechanical Systems)
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15 pages, 4161 KiB  
Article
Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines
by Natalia Espinoza-Sepulveda and Jyoti Sinha
Machines 2021, 9(8), 155; https://doi.org/10.3390/machines9080155 - 7 Aug 2021
Cited by 8 | Viewed by 2650
Abstract
Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in [...] Read more.
Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Mechanical Systems)
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