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Applied Sciences
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Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis—2nd Edition
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Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis—2nd Edition

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1316

Special Issue Editors

Dr. Janet Lin

E-Mail Website
Guest Editor
Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, T2064 Luleå, Sweden
Interests: operation and maintenance engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Liangwei Zhang

E-Mail Website
Guest Editor
Department of Industrial Engineering, Dongguan University of Technology, Dongguan 523808, China
Interests: fault prediction and health monitoring; anomaly detection; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haidong Shao

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the fourth industrial revolution, or Industry 4.0, a key objective is to enhance equipment's ability to perceive its own health state and predict future behavior. The development of artificial intelligence, especially the progress made in deep learning, over the last decade provides a promising tool in bolstering this enhancement. Such a tool can be a complement or alternative to conventional physics-based and signal-processing-based techniques in fault detection, diagnosis, and prognosis applications.

Researchers have started to build data-driven or hybrid models to further boost their prediction accuracy in the above applications, yet there are still some untouched or underexplored territories, such as causal inference, demystifying black-box modelling, domain adaptation, automatic feature learning, etc. This Special Issue aims to present current innovations and engineering achievements of scientists and industrial practitioners in the area of adopting artificial intelligence techniques in fault detection, diagnosis, and prognosis.

Topics of interest include, but are not limited to, the following:

  • Adoption of cutting-edge artificial intelligence in prognostics and health management (PHM).
  • Data-driven, physics-based, signal-processing-based, or hybrid models straddling the above counterparts.
  • Domain adaptation using transfer learning.
  • Demystifying the black-box and gaining new insights: interpretability of the learned models.
  • Knowledge distillation for edge-computing applications

Dr. Janet Lin
Dr. Liangwei Zhang
Dr. Haidong Shao
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. 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

  • adoption of cutting-edge artificial intelligence in prognostics and health management (PHM)
  • data-driven, physics-based, signal-processing-based, or hybrid models straddling the above counterparts
  • domain adaptation using transfer learning
  • demystifying the black-box and gaining new insights: interpretability of the learned models
  • knowledge distillation for edge-computing applications

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Published Papers (1 paper)

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Research

24 pages, 2042 KiB  
Article
A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem
by Ziyi Tang, Xinhao Hou, Xin Wang and Jifeng Zou
Appl. Sci. 2024, 14(16), 7254; https://doi.org/10.3390/app14167254 - 17 Aug 2024
Viewed by 1057
Abstract
With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. [...] Read more.
With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD) and conditional domain adversarial network with entropy conditioning (CDAN+E) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis—2nd Edition)
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