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Machines
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Advances in Intelligent Fault Diagnosis of Rotating Machinery
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Advances in Intelligent Fault Diagnosis of Rotating Machinery

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4356

Special Issue Editors

Prof. Dr. Wu Deng

E-Mail Website
Guest Editor
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: intelligent diagnosis; optimization algorithm; big data technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huimin Zhao

E-Mail Website
Guest Editor
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: feature extraction; fault diagnosis; signal analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ahmed Abu-Siada

E-Mail Website
Guest Editor
Discipline of Electrical and Computer Engineering, Curtin University, Perth, WA 6102, Australia
Interests: condition monitoring; fault diagnosis; asset management; power electronics; power system stability quality and control; renewable energy; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the demand and the development of science and technology, rotating machinery is developing to become more large-scale, complex and accurate, but it requires higher reliability. Prognostic and health management (PHM) is a new maintenance support technology, which is a comprehensive fault detection, isolation, prediction and health management technology. Intelligent fault diagnosis is the combination of artificial intelligence and fault diagnosis, which is mainly reflected in the application of domain expert knowledge and artificial intelligence technology in the diagnosis process. It is a system composed of human (especially domain experts) hardware capable of simulating brain functions, necessary external devices, physical devices and software supporting the hardware. It can quickly find and eliminate the faults according to the observed conditions, domain knowledge and experience as much as possible to improve the reliability of the rotating machinery. This Special Issue welcomes any original and high-quality papers but is not limited to the following:

  • Advanced Signal processing methods;
  • Feature extraction methods;
  • Data-driven fault diagnosis methods;
  • Advanced intelligence diagnosis techniques;
  • Advanced health monitoring techniques;
  • Deep learning and transfer learning;
  • Advanced machine learning algorithms;
  • Application in rotating machinery.

Prof. Dr. Wu Deng
Prof. Dr. Huimin Zhao
Prof. Dr. Ahmed Abu-Siada
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

  • signal processing
  • intelligent diagnosis
  • deep learning
  • transfer learning
  • artificial intelligence

Published Papers (3 papers)

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Research

22 pages, 4428 KiB  
Article
Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines
by Zachary Ward, Jordan Miller, Jeremiah Engel, Mohammad A. S. Masoum, Mohammad Shekaramiz and Abdennour Seibi
Machines 2024, 12(1), 55; https://doi.org/10.3390/machines12010055 - 12 Jan 2024
Viewed by 943
Abstract
Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve [...] Read more.
Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique. Full article
(This article belongs to the Special Issue Advances in Intelligent Fault Diagnosis of Rotating Machinery)
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17 pages, 14289 KiB  
Article
Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis
by Wei Zhang, Junxia Li, Shuai Huang, Qihang Wu, Shaowei Liu and Bin Li
Machines 2023, 11(5), 515; https://doi.org/10.3390/machines11050515 - 01 May 2023
Viewed by 1190
Abstract
Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the [...] Read more.
Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the collected vibration signals were transformed into images using the continuous wavelet transform. Subsequently, an MSCNN was designed to extract all detailed features of the original images. The final feature maps were obtained by fusing multiple feature layers. The parameters in the network were randomly generated and remained unchanged, which could effectively accelerate the calculation. Finally, an extreme learning machine was used to classify faults based on the fused feature maps, and the potential relationship between the fault and labels was established. The effectiveness of the proposed method was confirmed. This method performs better in mechanical fault diagnosis and classification than existing methods. Full article
(This article belongs to the Special Issue Advances in Intelligent Fault Diagnosis of Rotating Machinery)
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17 pages, 6096 KiB  
Article
Study on Aerodynamic Drag Reduction at Tail of 400 km/h EMU with Air Suction-Blowing Combination
by Hongjiang Cui, Guanxin Chen, Ying Guan and Wu Deng
Machines 2023, 11(2), 222; https://doi.org/10.3390/machines11020222 - 03 Feb 2023
Cited by 1 | Viewed by 1519
Abstract
In order to further reduce the aerodynamic drag of High-speed Electric Multiple Units (EMU), an active flow control drag reduction method combining air suction and blowing is proposed at the rear of the EMU train. A numerical calculation method based on realizable k-ε [...] Read more.
In order to further reduce the aerodynamic drag of High-speed Electric Multiple Units (EMU), an active flow control drag reduction method combining air suction and blowing is proposed at the rear of the EMU train. A numerical calculation method based on realizable k-ε is used to investigate the aerodynamic drag characteristics of a three-car EMU with a speed of 400 km/h. The influence of different suction-blowing mass flow rates, the position and number of suction and blowing ports on the aerodynamic drag and surface pressure of the EMU tail are analyzed. The results demonstrate that suction and blowing at the tail reduce the pressure drag of EMU. And with the growth of air suction-blowing mass flow rate, the aerodynamic drag reduction rate of the tail car gradually increases, but the increment of drag reduction rate gradually decreases. Under the same mass flow rate of the suction and blowing, the closer the ports are to the upper and lower edges of the windscreen, the lower the pressure drag of the tail car is. At the same flow flux of air suction and blowing, the more the number of ports, the better the pressure drag reduction effect of the tail car. This study provides a reference for the next generation of EMU aerodynamic drag reduction and is of great significance for breaking through the limitations of traditional aerodynamic drag reduction. Full article
(This article belongs to the Special Issue Advances in Intelligent Fault Diagnosis of Rotating Machinery)
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