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Deep Learning Based Intelligent Fault Diagnosis
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Deep Learning Based Intelligent Fault Diagnosis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 881

Special Issue Editors

Dr. Long Zhang

E-Mail Website
Guest Editor
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: signal processing and intelligent fault diagnosis of complex mechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayang Liu

E-Mail Website
Guest Editor
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: fault diagnosis; deep learning; transfer learning; anomaly detection
Dr. Xiaoli Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: intelligent diagnostics; prognostics and health management (PHM) for electromechanical and hydraulic systems; artificial intelligence and signal processing; digital twins and intelligent robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenghong Wu

E-Mail
Guest Editor
Xi’an Modern Control Technology Research Institute, Xi’an 710000, China
Interests: health monitoring; intelligent identification; transfer learning; deep learning

Special Issue Information

Dear Colleagues,

The rapid development of deep learning has significantly transformed various fields, including fault diagnosis in complex systems. Intelligent fault diagnosis, leveraging deep learning techniques, offers unprecedented opportunities to improve the reliability, safety, and efficiency of machinery and equipment. By harnessing deep learning, researchers can uncover intricate patterns, enhance fault identification accuracy, and adapt to diverse operational conditions, addressing challenges such as non-stationary signals, data scarcity, and cross-domain variability.

We are pleased to invite you to contribute to this Special Issue titled “Deep Learning Based Intelligent Fault Diagnosis” to share your innovative research and insights into this vital and evolving field.

This Special Issue aims to highlight recent advances in combining deep learning with sensing technologies, multi-sensor information fusion, and diagnostic techniques while emphasizing innovative solutions for real-world engineering problems and fostering multidisciplinary approaches to enhance system diagnostics. The scope of this Special Issue encompasses theoretical advances, algorithm development, and practical applications, including (but not limited to) the following topics of interest:

  • Novel deep learning architectures for fault diagnosis;
  • Explainable AI techniques in fault diagnosis;
  • Cross-domain fault diagnosis;
  • Real-time fault detection and prediction;
  • Data augmentation and imbalance handling in deep learning for fault diagnosis;
  • Case studies of deep learning-based fault diagnosis in industrial applications (e.g., railway vehicles, wind turbines, aerospace).

Dr. Long Zhang
Dr. Jiayang Liu
Dr. Xiaoli Zhao
Dr. Zhenghong Wu
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. Sensors 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 2600 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

  • fault diagnosis
  • deep learning
  • transfer learning
  • domain generalization
  • feature extraction
  • condition monitoring
  • fault prognostics
  • anomaly detection
  • condition assessment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 10080 KiB  
Article
Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis
by Jia Xu, Yan Wang, Renyi Xu, Hailin Wang and Xinzhi Zhou
Sensors 2025, 25(10), 3019; https://doi.org/10.3390/s25103019 - 10 May 2025
Viewed by 324
Abstract
In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. [...] Read more.
In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. The extracted features are fed into the Density Peak Clustering (DPC) module, which identifies known and unknown classes based on the computed local densities and relative distances. Finally, validation is performed on the Case Western Reserve University (CWRU) dataset, the Xi’an Jiaotong University rolling bearing accelerated life test dataset (XJTU-SY), and the Paderborn University bearing dataset (PU), Germany, and the framework is comprehensively evaluated in terms of several evaluation metrics, such as normalization accuracy and feature visualization. The experimental results show that SMCPL-ECA-DPC outperforms the comparative methods of SMCPL, CPL, ANEDL, CNN, and OpenMax and has high diagnostic performance in the identification of unknown faults. Full article
(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)
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16 pages, 6997 KiB  
Article
Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention
by Bin Yuan, Yaoqi Li and Suifan Chen
Sensors 2025, 25(9), 2636; https://doi.org/10.3390/s25092636 - 22 Apr 2025
Viewed by 383
Abstract
As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and [...] Read more.
As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. The lightweight convolutional attention is adaptively captured by the dynamic convolutional kernel generation strategy to adaptively capture local features in the time domain, and combined with grouped convolution to enhance the computational efficiency further; in addition, parameterized revised linear units are introduced to retain fault-sensitive negative information, which enhances the model’s ability to detect weak faults. The experimental findings demonstrate that the proposed model achieves an accuracy greater than 98.9%, highlighting its exceptional diagnostic accuracy and robustness. Moreover, compared to other fault diagnosis methods, the model exhibits superior performance under complex working conditions. Full article
(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)
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