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Machines
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Condition Monitoring for Non-stationary Rotating Machines
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Condition Monitoring for Non-stationary Rotating Machines

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 8267

Special Issue Editor

Dr. Davide Astolfi

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: wind turbines; condition monitoring; fault diagnosis; non-stationary machinery; control and monitoring; vibrations; applied statistics; numerical modelling; mechanical systems dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring of complex machines undergoing nonstationary operation conditions is a topical issue: Some widespread industry applications, for example, energy conversion systems, have stochastic sources, and therefore, the load conditions can be particularly unstable and unpredictable.

Signals acquired from machines having a complex design and operating in complex conditions contain contributions from several different components, as well as noise. Therefore, the major challenge of condition monitoring is to recover specific information about the signal components, in order to point out the signal content that is related to the state of the monitored component.

These facts have stimulated the most recent advances in signal processing techniques for vibrodiagnostics applied to nonstationary machines, for example, empirical mode decomposition and instantaneous angular wind speed. A pivotal role for rotating machines is played by gears and bearings: The condition monitoring task for this kind of components has stimulated appropriate signal processing techniques, based, for example, on the separation of the cyclostationary components of vibration signals.

On these grounds, this Special Issue aims at attracting contributions about theoretical and experimental developments about every possible aspect of condition monitoring for nonstationary rotating machines. Suitable topics include but are not limited to:

  • Rolling bearing diagnostics;
  • Geared systems diagnostics;
  • Signal processing;
  • Test rig and laboratory developments;
  • Numerical modeling of machine dynamics;
  • Case studies.

Dr. Davide Astolfi
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 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
  • machinery diagnostics
  • nonstationary conditions
  • rotating machines
  • signal processing

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Published Papers (2 papers)

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Research

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14 pages, 4696 KiB  
Article
Vibration and Stability Analysis of a Bearing–Rotor System with Transverse Breathing Crack and Initial Bending
by Yuehua Wang, Xin Xiong and Xiong Hu
Machines 2021, 9(4), 79; https://doi.org/10.3390/machines9040079 - 8 Apr 2021
Cited by 8 | Viewed by 3299
Abstract
This paper focuses on the stability and nonlinear response of a bearing-rotor system affected by a transverse crack and initial bending which was thought to be part of an unbalance or had been neglected before. The differences of breathing functions for the transverse [...] Read more.
This paper focuses on the stability and nonlinear response of a bearing-rotor system affected by a transverse crack and initial bending which was thought to be part of an unbalance or had been neglected before. The differences of breathing functions for the transverse breathing crack caused by initial bending is presented here, and the calculation of time-varying finite elements stiffness matrix of the cracked shaft is improved by replacing traditional the approximate crack segment with an exact area. After establishing the dynamic model of the cracked rotor with initial bending, vibrational characteristics such as amplitude-speed diagram, frequency spectrogram and bifurcations are investigated in detail. The eigenvalues of the transition matrix are calculated and analyzed as an indicator of dynamic stability with the growths of crack depth and initial bending. Many differences are found between the two cases of dynamic response of rotor system by numerical simulation. The frequency change with the growth of initial bending is opposite to the change with the growth of crack depth, and the shapes of amplitude-speed also having great different features. Stable regions are reduced and extended laterally by initial bending. All these results obtained in this paper will contribute to identify the bending fault and assess the stability of the bearing-rotor systems. Full article
(This article belongs to the Special Issue Condition Monitoring for Non-stationary Rotating Machines)
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Other

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17 pages, 3606 KiB  
Perspective
Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling
by Davide Astolfi
Machines 2021, 9(5), 100; https://doi.org/10.3390/machines9050100 - 13 May 2021
Cited by 18 | Viewed by 3761
Abstract
Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for [...] Read more.
Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated. Full article
(This article belongs to the Special Issue Condition Monitoring for Non-stationary Rotating Machines)
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