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Machine Condition Monitoring and Fault Diagnosis: From Theory to Application,...
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Machine Condition Monitoring and Fault Diagnosis: From Theory to Application, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2110

Special Issue Editor

Dr. Feiyun Cong

E-Mail Website
Guest Editor
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: fault diagnosis method; fault modeling; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern machines are becoming more structurally complex and operate under harsher loading and operational conditions. To ensure the efficient and reliable operation of machines, reduced unscheduled downtime, and lower operation and maintenance costs, it is necessary to develop intelligent fault diagnostic methods and assess their health state for the aim of identifying the mode, type, severity, and degradation trend of faults.

This Special Issue encourages and welcomes original research articles on machine fault detection, diagnosis, and prognosis. Potential topics include, but are not limited to, the following:

  • Fault diagnosis methods based on various sensor data;
  • Signal processing;
  • Fault model research with changeable variable transfer path;
  • Fault diagnostics under non-stationary operating conditions;
  • Fault prediction;
  • Machine-learning-based fault diagnostics and condition monitoring;
  • Fatigue analysis of machinery.

Dr. Feiyun Cong
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. Applied Sciences 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 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
  • fault diagnosis
  • fault modeling

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Published Papers (2 papers)

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Research

29 pages, 7672 KiB  
Article
A Robust Wind Turbine Component Health Status Indicator
by Roberto Lázaro, Julio J. Melero and Nurseda Y. Yürüşen
Appl. Sci. 2024, 14(16), 7256; https://doi.org/10.3390/app14167256 - 17 Aug 2024
Viewed by 945
Abstract
Wind turbine components’ failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine’s component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection [...] Read more.
Wind turbine components’ failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine’s component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component’s health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component’s status in a multivariate environment, providing the selected variables’ joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application, 2nd Edition)
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20 pages, 7666 KiB  
Article
Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP
by Hao Chang, Feng Gao, Yan Li and Lihong Chang
Appl. Sci. 2024, 14(10), 4314; https://doi.org/10.3390/app14104314 - 20 May 2024
Viewed by 696
Abstract
Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article [...] Read more.
Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application, 2nd Edition)
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