Special Issue "Multi-Body System Dynamics: Monitoring, Simulation and Control"

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (31 October 2018) | Viewed by 11928

Special Issue Editor

Prof. Dr. Kari Tammi
E-Mail Website
Guest Editor
Department of Mechanical Engineering, Aalto University, Espoo, Finland
Interests: mechatronics; electric machines; energy efficiency; dynamics; control; adaptive systems; new machine concepts; innovation management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mechanical systems are becoming more and more connected with electric power transmission, monitoring, automation, and control systems. The multi-physical connections take place over various networks, e.g., power networks or industrial Internet. The motivation for the connection is usually in improving machine efficiency, safety, or environmental impact. The machine design becomes challenging, as mastering one discipline cannot guarantee machine behavior.

This Special Issue calls original research papers on multi-body mechanical systems connected with electric powertrain, control and/or monitoring systems. These mechatronic systems can be found in various applications, e.g., in electric or autonomous vehicles, robotics, etc. The scope of this issue is not limited to the application focus, but, rather, calls for novel contributions on the analysis of these multi-physical systems. Multi-physical analysis of multi-body systems connected with experimental results is expected in papers that will be published.  

Prof. Dr. Kari Tammi
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. 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 1800 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

  • Multi-physical systems
  • Energy efficiency, electric powertrain
  • Autonomous systems
  • Industrial Internet, IoT

Published Papers (3 papers)

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Research

Article
Full-Scale Wind Turbine Vibration Signature Analysis
Machines 2018, 6(4), 63; https://doi.org/10.3390/machines6040063 - 07 Dec 2018
Cited by 13 | Viewed by 2582
Abstract
A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was [...] Read more.
A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox. Full article
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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Article
Tether Space Mobility Device Attitude Control during Tether Extension and Winding
Machines 2018, 6(4), 61; https://doi.org/10.3390/machines6040061 - 22 Nov 2018
Cited by 1 | Viewed by 1378
Abstract
Recently, advancements in space technology have opened up more opportunities for human beings to work in outer space. It is expected that upsizing of manned space facilities, such as the International Space Station, will further this trend. Therefore, a unique means of transportation [...] Read more.
Recently, advancements in space technology have opened up more opportunities for human beings to work in outer space. It is expected that upsizing of manned space facilities, such as the International Space Station, will further this trend. Therefore, a unique means of transportation is necessary to ensure that human beings can move about effectively in microgravity environments. In the present study, we propose a tether-based mobility system, which moves the user by winding a tether attached to a structure at the destination. However, there is a problem in that the attitude of the user becomes unstable during winding of the tether. Therefore, a Tether Space Mobility Device (TSMD) attitude control method for winding a tether is examined through numerical analysis. The proposed analytical model consists of one flexible body and three rigid bodies. The contact force between the tether and the inlet is considered. We verified the validity of the proposed model through experiments. Furthermore, we proposed a TSMD attitude control method during tether winding while focusing on changes in the system’s rotational kinetic energy. Using the proposed analytical model, the angular velocity of a rigid body system is confirmed to converge to 0 deg/s when control is applied. Full article
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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Article
Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement
Machines 2018, 6(3), 38; https://doi.org/10.3390/machines6030038 - 01 Sep 2018
Cited by 67 | Viewed by 7615
Abstract
This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that [...] Read more.
This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it. Full article
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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