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Machines
Special Issues
Data-Driven and AI-Based Fault Diagnosis for Industrial Dynamic Systems
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Data-Driven and AI-Based Fault Diagnosis for Industrial Dynamic Systems

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 1160

Editors

Dr. Ping Wu

E-Mail Website
Guest Editor
School of Information Science and Engineering, ZheJiang Sci-Tech University, No. 928, 2nd Street, Xiasha Higher Education Park, Hangzhou 310018, China
Interests: fault diagnosis; machine learning; industrial intelligence
Dr. Qian Zhang

E-Mail Website
Guest Editor
School of Engineering, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: data-driven modeling; multi-objective optimal design; robotics and control; their applications in engineering and transport systems
Dr. Siwei Lou

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
Interests: AI-enabled industrial data analytics; signal processing; automation control; smart manufacturing; process monitoring; blast furnace ironmaking process
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity and automation of industrial dynamic systems demand a paradigm shift from traditional model-based fault diagnosis towards intelligent, data-driven methodologies. The convergence of advanced sensing, big data, and artificial intelligence (AI) offers unprecedented opportunities for developing more accurate, adaptive, and proactive fault detection, isolation, and prognosis systems for dynamic industrial assets. This transformation is pivotal for enhancing operational safety, reliability, and efficiency within the Industry 4.0 framework.

This Special Issue aims to compile cutting-edge research and review articles that advance the theory and application of data-driven and AI-based solutions for fault diagnosis in industrial dynamic systems. It directly aligns with the journal Machines's scope regarding the design, control, monitoring, and intelligent maintenance of mechanical and electromechanical systems, focusing on practical engineering applications and smart technology integration.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Deep learning and neural network approaches for fault detection, isolation, and prognosis.
  • Hybrid models combining data-driven and physics-based insights.
  • Feature engineering and representation learning from sensor data.
  • Transfer learning and explainable AI for diagnostic models.
  • Real-time anomaly detection and prognostic health management (PHM).
  • Applications in robotics, wind turbines, automotive systems, and industrial processes.

We look forward to hearing from you

Dr. Ping Wu
Dr. Qian Zhang
Dr. Siwei Lou
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-anonymized 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

  • fault diagnosis
  • condition monitoring
  • prognostics and health management (PHM)
  • artificial intelligence (AI)
  • deep learning
  • dynamic systems

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Published Papers (1 paper)

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22 pages, 35239 KB  
Article
TBDDQN: Imbalanced Fault Diagnosis for Blast Furnace Ironmaking Process via Transformer–BiLSTM Double Deep Q-Networks
by Jinlong Zheng, Ping Wu, Ruirui Zuo, Xin Su, Yinzhu Liu and Nabin Kandel
Machines 2026, 14(3), 276; https://doi.org/10.3390/machines14030276 - 2 Mar 2026
Viewed by 638
Abstract
The blast furnace ironmaking process (BFIP) is a highly complex and dynamic industrial system where strong spatiotemporal coupling and severe data imbalance pose substantial challenges for fault diagnosis. To address these issues, this study proposes a Transformer–BiLSTM Double Deep Q-Network (TBDDQN) framework for [...] Read more.
The blast furnace ironmaking process (BFIP) is a highly complex and dynamic industrial system where strong spatiotemporal coupling and severe data imbalance pose substantial challenges for fault diagnosis. To address these issues, this study proposes a Transformer–BiLSTM Double Deep Q-Network (TBDDQN) framework for intelligent fault diagnosis. The framework employs a dual-branch architecture that integrates a Transformer-based spatial encoder with a BiLSTM-attention temporal extractor to capture global dependencies and dynamic patterns from multivariate time-series data. To mitigate class imbalance and asymmetric fault costs, a cost-sensitive reinforcement learning scheme based on Double DQN is incorporated, featuring prioritized experience replay and adaptive misclassification penalties. Experiments on real blast furnace datasets show that TBDDQN achieves a macro-averaged precision of 0.970 and a macro-averaged F1-score of 0.929, outperforming conventional CNN, LSTM, and DQN-based baselines. These results demonstrate that TBDDQN offers a robust and interpretable solution for imbalanced industrial fault diagnosis in the BFIP. Full article
(This article belongs to the Special Issue Data-Driven and AI-Based Fault Diagnosis for Industrial Dynamic Systems)
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