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Applied Sciences
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AI and Data-Driven Methods for Fault Detection and Diagnosis
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AI and Data-Driven Methods for Fault Detection and Diagnosis

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

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1898

Special Issue Editors

Prof. Dr. Yiwei Cheng

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

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: equipment failure prediction and health management; industrial artificial intelligence; structural health monitoring

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to recent developments and breakthroughs in intelligent systems monitoring, fault detection, and diagnosis. It seeks submissions on 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 diverse industrial applications are particularly encouraged.

The aim is to bring together researchers and practitioners from multidisciplinary fields to share their insights, foster collaboration, and promote the adoption of advanced intelligent diagnostic tools. All submissions will undergo rigorous peer review to ensure high standards of quality, originality, and practical relevance. This Special Issue will be widely promoted to maximize visibility and citation impact, contributing to the ongoing discourse in intelligent fault detection and diagnosis.

Prof. Dr. Yiwei Cheng
Dr. Zuoyi Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • signal processing for fault detection
  • fault diagnosis
  • predictive maintenance
  • artificial intelligence in diagnostics
  • equipment reliability
  • condition monitoring
  • real-time diagnostic systems
  • prognostics and health management (PHM)
  • data-driven fault detection

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Published Papers (2 papers)

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Research

18 pages, 2702 KB  
Article
A Dual-Branch Ensemble Learning Method for Industrial Anomaly Detection: Fusion and Optimization of Scattering and PCA Features
by Jing Cai, Zhuo Wu, Runan Hua, Shaohua Mao, Yulun Zhang, Ran Guo and Ke Lin
Appl. Sci. 2026, 16(3), 1597; https://doi.org/10.3390/app16031597 - 5 Feb 2026
Viewed by 500
Abstract
Industrial visual anomaly detection remains challenging because practical inspection systems must achieve high detection accuracy while operating under highly imbalanced data, diverse defect patterns, limited computational resources, and increasing demands for interpretability. This work aims to develop a lightweight yet effective and explainable [...] Read more.
Industrial visual anomaly detection remains challenging because practical inspection systems must achieve high detection accuracy while operating under highly imbalanced data, diverse defect patterns, limited computational resources, and increasing demands for interpretability. This work aims to develop a lightweight yet effective and explainable anomaly detection framework for industrial images in settings where a limited number of labeled anomalous samples are available. We propose a dual-branch feature-based supervised ensemble method that integrates complementary representations: a PCA branch to capture linear global structure and a scattering branch to model multi-scale textures. A heterogeneous pool of classical learners (SVM, RF, ET, XGBoost, and LightGBM) is trained on each feature branch, and stable probability outputs are obtained via stratified K-fold out-of-fold training, probability calibration, and a quantile-based threshold search. Decision-level fusion is then performed by stacking, where logistic regression, XGBoost, and LightGBM serve as meta-learners over the out-of-fold probabilities of the selected top-K base learners. Experiments on two public benchmarks (MVTec AD and BTAD) show that the proposed method substantially improves the best PCA-based single model, achieving relative F1_score gains of approximately 31% (MVTec AD) and 26% (BTAD), with maximum AUC values of about 0.91 and 0.96, respectively, under comparable inference complexity. Overall, the results demonstrate that combining high-quality handcrafted features with supervised ensemble fusion provides a practical and interpretable alternative/complement to heavier deep models for resource-constrained industrial anomaly detection, and future work will explore more category-adaptive decision strategies to further enhance robustness on challenging classes. Full article
(This article belongs to the Special Issue AI and Data-Driven Methods for Fault Detection and Diagnosis)
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21 pages, 6061 KB  
Article
DFed-LT: A Decentralized Federated Learning with Lightweight Transformer Network for Intelligent Fault Diagnosis
by Keqiang Xie, Cheng Cheng, Yiwei Cheng, Yuanhang Wang, Liping Chen, Wen Wen and Wei Shang
Appl. Sci. 2025, 15(21), 11484; https://doi.org/10.3390/app152111484 - 27 Oct 2025
Cited by 1 | Viewed by 1048
Abstract
In recent years, deep learning has been increasingly applied in the field of fault diagnosis, but it currently faces two challenges: (1) data privacy issues prevent the aggregation of data from different users to form a large training dataset; (2) the limited memory [...] Read more.
In recent years, deep learning has been increasingly applied in the field of fault diagnosis, but it currently faces two challenges: (1) data privacy issues prevent the aggregation of data from different users to form a large training dataset; (2) the limited memory of edge devices or handheld detection devices restricts the application of some larger structural models. To address these issues, this article proposes a lightweight federated learning method with transformer network for intelligent fault diagnosis. A federated learning architecture is constructed to achieve distributed learning of different user data, which not only ensures the privacy and security of user data, but also enables feature learning of different user data. In addition, the lightweight transformer network is built locally for different users to achieve the applicability of the model on different devices. An experimental case was implemented to demonstrate the effectiveness of the proposed method, and the results showed that the proposed method can achieve effective fault diagnosis while preserving data privacy. Compared with other methods, the proposed diagnostic model requires less computing resources. In addition, even under noisy conditions, the method maintains significant robustness against acoustic interference. Full article
(This article belongs to the Special Issue AI and Data-Driven Methods for Fault Detection and Diagnosis)
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