Special Issue "Symmetry in Mechanical Engineering"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 30 November 2019

Special Issue Editors

Guest Editor
Prof. Dr. Adam Glowacz

Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
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Interests: machine; fault diagnosis; pattern recognition; signal processing; signal analysis; image processing; computer science; automatic
Guest Editor
Prof. Dr. Grzegorz Krolczyk

Department of Manufacturing Engineering and Production Automation, Opole University of Technology, 45-758 Opole, Poland
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Interests: fault diagnosis; vibration analysis; measurement; materials
Guest Editor
Prof. Zhixiong Li

School of Mechanical & Manufacturing Engineering The University of New South Wales, Sydney (Australia)
Website | E-Mail
Interests: fault diagnosis; vibration analysis; measurement; mechanical engineering; diesel engines
Guest Editor
Prof. Dr. Jose Alfonso Antonino Daviu

Universitat de València: VALENCIA, Spain
Website | E-Mail
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms

Special Issue Information

Dear Colleagues,

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in mechanical engineering: measurement, fault diagnosis, construction, operation and maintenance of machines, vibration, noise, smart-material systems, integrated systems,  stresses, deformations, mechanical properties, signal processing of mechanical systems, fault diagnosis of machines, shafts, springs, belts, bearings, gears, rotors, rotor dynamics, and machine elements. This Special Issue encompasses applications in mechanical engineering, modelling methods for rigid-body mechanics, structural mechanics, impact mechanics, strain localization, tribology, and thermodynamics. Review articles related to mechanical engineering are also encouraged.

Prof. Dr. Adam Glowacz
Prof. Dr. Grzegorz Krolczyk
Prof. Zhixiong Li
Prof. Dr. Jose Alfonso Antonino Daviu
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 papers will be 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. Symmetry 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 1400 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

  • deformation
  • stresses
  • mechanical properties
  • tribology
  • thermodynamic
  • measurement
  • fault diagnosis
  • machine

Published Papers (8 papers)

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Research

Open AccessArticle
Data-Driven Adaptive Iterative Learning Method for Active Vibration Control Based on Imprecise Probability
Symmetry 2019, 11(6), 746; https://doi.org/10.3390/sym11060746
Received: 25 April 2019 / Revised: 27 May 2019 / Accepted: 29 May 2019 / Published: 2 June 2019
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Abstract
A data-driven adaptive iterative learning (IL) method is proposed for the active control of structural vibration. Considering the repeatability of structural dynamic responses in the vibration process, the time-varying proportional-type iterative learning (P-type IL) method was applied for the design of feedback controllers. [...] Read more.
A data-driven adaptive iterative learning (IL) method is proposed for the active control of structural vibration. Considering the repeatability of structural dynamic responses in the vibration process, the time-varying proportional-type iterative learning (P-type IL) method was applied for the design of feedback controllers. The model-free adaptive (MFA) control, a data-driven method, was used to self-tune the time-varying learning gains of the P-type IL method for improving the control precision of the system and the learning speed of the controllers. By using multi-source information, the state of the controlled system was detected and identified. The square root values of feedback gains can be considered as characteristic parameters and the theory of imprecise probability was investigated as a tool for designing the stopping criteria. The motion equation was driven from dynamic finite element (FE) formulation of piezoelectric material, and then was linearized and transformed properly to design the MFA controller. The proposed method was numerically and experimentally tested for a piezoelectric cantilever plate. The results demonstrate that the proposed method performs excellent in vibration suppression and the controllers had fast learning speeds. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
Open AccessArticle
A Method to Determine Core Design Problems and a Corresponding Solution Strategy
Symmetry 2019, 11(4), 576; https://doi.org/10.3390/sym11040576
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 16 April 2019 / Published: 19 April 2019
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Abstract
The lack of information on the correlation between root causes and corresponding control criteria in the importance calculation of root causes of design problems results in less accurate determinations of core problems. Based on the interaction between customer needs, bad product parameters, and [...] Read more.
The lack of information on the correlation between root causes and corresponding control criteria in the importance calculation of root causes of design problems results in less accurate determinations of core problems. Based on the interaction between customer needs, bad product parameters, and root causes, a hierarchical representation model of the design problem is established in this paper. A network layer of bad parameters, including various types of correlations, and a control layer, including technical feasibility and cost, are constructed. Then, a method based on the network analytic hierarchy process is proposed to rank the importance of root causes of the design problem and determine the core problems. Finally, a product design process based on the core problem solving is established to assist designers with improving design quality and efficiency. The design for the coolant flow distribution device in the lower chamber of a third-generation pressurized water reactor is employed as an example to demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data
Symmetry 2019, 11(2), 188; https://doi.org/10.3390/sym11020188
Received: 30 November 2018 / Revised: 30 January 2019 / Accepted: 31 January 2019 / Published: 8 February 2019
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Abstract
In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable [...] Read more.
In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable of identifying these patterns of moving objects based on their dynamic attributes and historical data. First of all, based on dependable prior data, likelihood information about objects of interest is obtained in terms of dynamic attributes, such as speed, direction and position. Observations on these attributes of a new object can be obtained as pieces of evidence profiled as probability distributions or generally belief distributions if ambiguity appears in the observations. Using likelihood modelling, the observed pieces of evidence are independent of the prior distribution patterns. Subsequently, Dempster’s rule is used to combine the pieces of evidence under consideration of their weight and reliability to identify the moving object. A real world case study of maritime radar surveillance is conducted to validate and prove the efficiency of the proposed approach. Overall, this approach is capable of providing a probabilistic and rigorous recognition result for pattern recognition of moving objects, which is suitable for any other actively detecting applications in transportation systems. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
Matching Model of Dual Mass Flywheel and Power Transmission Based on the Structural Sensitivity Analysis Method
Symmetry 2019, 11(2), 187; https://doi.org/10.3390/sym11020187
Received: 6 December 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 7 February 2019
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Abstract
As a new torsional vibration absorber, the dual mass flywheel (DMF) contains a symmetric structure in which the damping element is a pair of springs symmetrically distributed along the circumference direction. Through reasonable matching parameters, the DMF functions in isolating torsional vibrations caused [...] Read more.
As a new torsional vibration absorber, the dual mass flywheel (DMF) contains a symmetric structure in which the damping element is a pair of springs symmetrically distributed along the circumference direction. Through reasonable matching parameters, the DMF functions in isolating torsional vibrations caused by the engine from the transmission system. Our work aims to solve the accuracy of matching models between the DMF and power transmission system. The critical structural parameters of each order modal are treated consecutively by two methods: Absolute sensitivity (e.g., under the idle condition and driving condition), and relative sensitivity. The operation achieves a separation of the parameters and diagnosis of the relationship between these parameters and the natural frequency in the system. In addition, the natural frequency range is determined based upon the area of the resonance speed. As a result, the matching model is established based on the sensitivity analysis method and the natural frequency range, which means the moment of inertia distribution (its coefficient should be used as one structural parameter in relative sensitivity analysis) and the torsional stiffness in multiple stages can be observed under the combined values. The effectiveness of the matching model is verified by experiments of a real vehicle test under the idling condition and driving condition. It is concluded that the analysis study can be applied to solve the parameters matching accuracy among certain multi-degree-of-freedom dynamic models. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
On the Identification of Sectional Deformation Modes of Thin-Walled Structures with Doubly Symmetric Cross-Sections Based on the Shell-Like Deformation
Symmetry 2018, 10(12), 759; https://doi.org/10.3390/sym10120759
Received: 19 November 2018 / Revised: 10 December 2018 / Accepted: 11 December 2018 / Published: 16 December 2018
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Abstract
In this paper, a new approach is proposed to identify sectional deformation modes of the doubly symmetric thin-walled cross-section, which are to be employed in formulating a one-dimensional model of thin-walled structures. The approach considers the three-dimensional displacement field of the structure as [...] Read more.
In this paper, a new approach is proposed to identify sectional deformation modes of the doubly symmetric thin-walled cross-section, which are to be employed in formulating a one-dimensional model of thin-walled structures. The approach considers the three-dimensional displacement field of the structure as the linear superposition of a set of sectional deformation modes. To retrieve these modes, the modal analysis of a thin-walled structure is carried out based on shell/plate theory, with the shell-like deformation shapes extracted. The components of classical modes are removed from these shapes based on a novel criterion, with residual deformation shapes left. By introducing benchmark points, these shapes are further classified into several deformation patterns, and within each pattern, higher-order deformation modes are derived by removing the components of identified ones. Considering the doubly symmetric cross-section, these modes are approximated with shape functions applying the interpolation method. The identified modes are finally used to deduce the governing equations of the thin-walled structure, applying Hamilton’s principle. Numerical examples are also presented to validate the accuracy and efficiency of the new model in reproducing three-dimensional behaviors of thin-walled structures. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
Robust Adaptive Full-Order TSM Control Based on Neural Network
Symmetry 2018, 10(12), 726; https://doi.org/10.3390/sym10120726
Received: 13 November 2018 / Revised: 2 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
Cited by 1 | PDF Full-text (4484 KB) | HTML Full-text | XML Full-text
Abstract
Existing full-order terminal sliding mode (FOTSM) control methods often require a priori knowledge of the system model. To tackle this problem, two novel neural-network-based FOTSM control methods were proposed. The first one was model based but did not require knowledge of the uncertainties’ [...] Read more.
Existing full-order terminal sliding mode (FOTSM) control methods often require a priori knowledge of the system model. To tackle this problem, two novel neural-network-based FOTSM control methods were proposed. The first one was model based but did not require knowledge of the uncertainties’ bounds. The second one was model free and did not require knowledge of the system model. Finite-time convergence of the two schemes was verified by theoretical analysis and simulation cases. Meanwhile, the designed methods avoided singularity as well as chattering. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction
Symmetry 2018, 10(12), 684; https://doi.org/10.3390/sym10120684
Received: 25 October 2018 / Revised: 13 November 2018 / Accepted: 14 November 2018 / Published: 1 December 2018
Cited by 2 | PDF Full-text (4333 KB) | HTML Full-text | XML Full-text
Abstract
A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational [...] Read more.
A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational mode decomposition (VMD) method, then an adaptive VMD (called DTCFVMD) method is obtained to extract the fault feature. In the DTCFVMD method, a center frequency method of double thresholds is a symmetry method, which is used to determine the decomposed mode number of VMD according to the power spectrum of the signal. The proposed DTCFVMD method is used to decompose the nonlinear and non-stationary vibration signals of motor bearing in order to obtain a series of intrinsic mode functions (IMFs) under different scales. Then, the Hilbert transform is used to analyze the envelope of each mode component and calculate the power spectrum of each mode component. Finally, the power spectrum is used to extract the fault feature frequency for determining the fault type of the motor bearing. To test and verify the effectiveness of the DTCFVMD method, the actual fault vibration signal of the motor bearing is selected in here. The experimental results show that the center frequency method of double thresholds can effectively determine the mode number of the VMD method, and the proposed DTCFVMD method can accurately extract the clear time frequency characteristics of each mode component, and obtain the fault characteristics of characteristics; frequency, rotating frequency, and frequency doubling and so on. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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Open AccessArticle
On the Existence of Self-Excited Vibration in Thin Spur Gears: A Theoretical Model for the Estimation of Damping by the Energy Method
Symmetry 2018, 10(12), 664; https://doi.org/10.3390/sym10120664
Received: 26 October 2018 / Revised: 21 November 2018 / Accepted: 22 November 2018 / Published: 22 November 2018
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Abstract
The gear is a cyclic symmetric structure, and each tooth is subjected to a periodic mesh force. These mesh forces have the same phase difference tooth by tooth, which can excite gear vibrations. The mechanism of additional axial force caused by gear bending [...] Read more.
The gear is a cyclic symmetric structure, and each tooth is subjected to a periodic mesh force. These mesh forces have the same phase difference tooth by tooth, which can excite gear vibrations. The mechanism of additional axial force caused by gear bending is shown and examined, which can significantly affect the stability of a self-excited thin spur gears vibration. A mechanical model based on energy balance is then developed to predict the contribution of additional axial force, leading to the proposed numerical integration method for vibration stability analysis. By analyzing the change in the system energy, the occurrence of the self-excited vibration is validated. A numerical simulation is carried out to verify the theoretical analysis. The impacts of modal damping, contact ratio, and the number of nodal diameters on the stability boundaries of the self-excited vibration are revealed. The results prove that the backward traveling wave of the driven gear as well as the forward traveling wave of the driving gear encounter self-excited vibration in the absence of sufficient damping. The model can be used to predict the stability of the gear self-excited vibration. Full article
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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