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Applied Sciences
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Advances in Intelligent Monitoring and Fault Diagnosis of Mechanical Equipment
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Advances in Intelligent Monitoring and Fault Diagnosis of Mechanical Equipment

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 438

Special Issue Editor

Prof. Dr. Yiwei Cheng

E-Mail Website
Guest Editor
School of Mechanical and Electronic Information, China University of Geosciences, Wuhan 430078, China
Interests: big data analytics; machining health monitoring; intelligent fault diagnosis; remaining useful life prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Advances in Intelligent Monitoring and Fault Diagnosis of Mechanical Equipment", is dedicated to exploring recent developments and breakthroughs in the field of intelligent systems for monitoring, diagnosing, and predicting mechanical equipment health. This Special Issue requests submissions that cover innovative technologies and methodologies for fault detection, real-time monitoring, predictive maintenance, and data-driven diagnosis using artificial intelligence, machine learning, and signal processing techniques. Contributions that address the challenges of improving equipment reliability, reducing downtime, and optimizing performance across various industrial applications are particularly encouraged.

We will bring together researchers and practitioners from multidisciplinary fields to share their insights, foster collaboration, and promote the adoption of advanced intelligent diagnostic tools. All articles will undergo a rigorous peer review process to ensure high standards of quality, originality, and practical relevance. This Special Issue will be widely promoted to maximize visibility and citation impact and contribute to the ongoing discourse regarding intelligent fault diagnosis and mechanical equipment maintenance.

Prof. Dr. Yiwei Cheng
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

  • intelligent monitoring
  • fault diagnosis
  • mechanical equipment health
  • predictive maintenance
  • artificial intelligence in diagnostics
  • machine learning applications
  • signal processing for fault detection
  • equipment reliability
  • condition monitoring
  • real-time diagnostic systems
  • vibration analysis
  • prognostics and health management (PHM)
  • data-driven fault detection
  • deep learning for mechanical systems
  • industrial equipment maintenance

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Published Papers (1 paper)

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Research

16 pages, 4168 KiB  
Article
Fault Diagnosis of Wind Turbine Blades Based on One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting and Multi-Source Data Fusion
by Kangqiao Ma, Yongqian Wang and Yu Yang
Appl. Sci. 2025, 15(7), 3440; https://doi.org/10.3390/app15073440 - 21 Mar 2025
Viewed by 249
Abstract
To prevent wind turbine blade accidents and improve fault detection accuracy, a hybrid deep learning model based on 1D CNN-BiLSTM-AdaBoost for wind turbine-blade fault classification is proposed. Fault data are first preprocessed by segmenting and labeling the fault patterns. Features are extracted through [...] Read more.
To prevent wind turbine blade accidents and improve fault detection accuracy, a hybrid deep learning model based on 1D CNN-BiLSTM-AdaBoost for wind turbine-blade fault classification is proposed. Fault data are first preprocessed by segmenting and labeling the fault patterns. Features are extracted through the convolutional layers, followed by dimensionality reduction and denoising using the pooling layers, and feature fusion. The multi-source sensor features are then fed into the BiLSTM layer for further processing of the time-series characteristics. The processed data are classified through a fully connected layer. Finally, multiple weak classifiers are combined to generate the final classification result. Experimental results show that the 1D CNN-BiLSTM-AdaBoost model outperforms models that use only 1D CNN, BiLSTM, and 1D CNN-BiLSTM, achieving an accuracy of 96.88%, precision of 97.22%, recall of 96.92%, and an F1 score of 96.86%, with a maximum accuracy of 100%. These results validate the model’s effectiveness for fault classification. Full article
(This article belongs to the Special Issue Advances in Intelligent Monitoring and Fault Diagnosis of Mechanical Equipment)
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