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Machines
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Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex...
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Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 2937

Special Issue Editors

Dr. Khandaker Noman

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: predictive maintenance; digital twin; signal processing; machine learning; system reliability analysis; remaining useful life prediction; time–frequency analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Shabbir Ahmed

E-Mail Website
Guest Editor
Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA
Interests: adaptive and time varying systems; stochastic signals and systems; structural health monitoring; structural mechanics and dynamics; random vibrations
Dr. Yang Yu

E-Mail Website
Guest Editor
Aircraft Strength Research Institute, Xi’an 710065, China
Interests: stress analysis and testing of composite material structures; intelligent structural design; structural health monitoring
Prof. Dr. Anayet U. Patwari

E-Mail Website
Guest Editor
Professor and Dean, Faculty of Engineering and Technology, Islamic University of Technology, Board Bazar, Gazipur 1704, Dhaka, Bangladesh
Interests: smart machining; green engineering; condition monitoring; artificial intelligence; optimization; finite element analysis
Prof. Dr. Yongbo Li

E-Mail Website
Guest Editor
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modeling of mechanical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of big data and industry 4.0-related technologies, data-driven approaches have gained significant attention among researchers from the area of the health management of complex mechanical structures. Data collected from these complex mechanical structures are a source of important information regarding the health condition of a particular structure. As a result, the collected data can not only be utilised to perform the modelling of the health status of that structure but also be used to diagnose different types of damage in a structure. Additionally, data-driven prognosis operation can be used to detect damage at the earliest point of inception, track the growth of the incepted damage, and predict the remaining useful life of the structure under analysis. By making full use of the data-driven approaches, the decision maker can not only identify the problem of mechanical structures in a convenient manner but also perform necessary maintenance actions prior to failure of the structure under consideration. In this context, data-driven approaches have emerged as some of the key enablers in improving safety, increasing operational reliability and mission availability, decreasing unnecessary maintenance actions, and reducing system life-cycle costs. All these aspects have made data-driven technologies a research hotspot for a variety of application areas corresponding to structural health management including transportation, aviation, aerospace, manufacturing, mining, railways, renewable energy generation, and so on.

Considering the diverse and considerable state-of-the-art research status and related prospects of the data-driven approaches with regard to modelling the complex structural health condition, performing diagnosis operation on the faulty complex structure, and conducting prognostic operation to identify the failure event as well as the corresponding future degradation phenomenon, this Special Issue aims to invite researchers from all aspects of the health management of complex mechanical structures to submit their work. Potential topics related to the health management of complex mechanical structures include but are not limited to the following:

  1. Digital twin technology;
  2. Condition monitoring;
  3. Fault diagnosis;
  4. Early fault detection;
  5. Fault severity analysis;
  6. Remaining useful life prediction;
  7. Dynamic modelling;
  8.  Signal processing algorithms;
  9. Applied machine learning;
  10. Deep learning techniques;
  11.  Structural health monitoring;
  12. Information fusion;
  13. Advanced sensing techniques;
  14. Dimensionality reduction;
  15. Feature extraction.

Dr. Khandaker Noman
Dr. Shabbir Ahmed
Dr. Yang Yu
Prof. Dr. Anayet U. Patwari
Prof. Dr. Yongbo Li
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.

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Published Papers (2 papers)

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Research

22 pages, 7423 KiB  
Article
Advancing UAV Sensor Fault Diagnosis Based on Prior Knowledge and Graph Convolutional Network
by Hui Li, Chaoyin Chen, Tiancai Wan, Shaoshan Sun, Yongbo Li and Zichen Deng
Machines 2024, 12(10), 716; https://doi.org/10.3390/machines12100716 - 10 Oct 2024
Cited by 1 | Viewed by 1071
Abstract
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have [...] Read more.
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV’s dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. Full article
(This article belongs to the Special Issue Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures)
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22 pages, 6532 KiB  
Article
Predictive Analysis of Crack Growth in Bearings via Neural Networks
by Manpreet Singh, Dharma Teja Gopaluni, Sumit Shoor, Govind Vashishtha and Sumika Chauhan
Machines 2024, 12(9), 607; https://doi.org/10.3390/machines12090607 - 1 Sep 2024
Viewed by 1159
Abstract
Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts [...] Read more.
Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate. Full article
(This article belongs to the Special Issue Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures)
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