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Machines
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Prognostics and Health Management and Structural Health Management in Mechanical...
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Prognostics and Health Management and Structural Health Management in Mechanical and Manufacturing Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2206

Special Issue Editor

Dr. Izaz Raouf

E-Mail Website
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea
Interests: prognostics and health management (PHM); AI in mechanical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue on Prognostics and Health Management (PHM) and Structural Health Management (SHM) in mechanical and manufacturing systems. This issue aims to bring together experts and researchers from academia and industry to highlight the latest advancements and challenges in PHM and SHM, particularly in the context of aerospace technology and smart factories.

As technology continues to revolutionize the mechanical and manufacturing industries, PHM and SHM have become increasingly critical. Smart factories, with their integration of AI and automation, have opened up new possibilities for optimizing maintenance strategies, enhancing productivity, and prolonging the health and performance of mechanical systems. Similarly, in aerospace technology, SHM for composite materials is essential for ensuring the safety, reliability, and longevity of advanced aerospace structures.

We invite researchers and professionals to contribute their original research and share their insights on topics related to PHM and SHM in mechanical and manufacturing systems, including, but not limited to, the following:

  • The Applications of AI in PHM and SHM for Mechanical Engineering.
  • PHM and SHM in Smart Factories: Challenges and Opportunities.
  • SHM for Composite Materials in Aerospace Applications.
  • Predictive Maintenance Techniques for Mechanical Systems.
  • Prognostics and Health Monitoring in Manufacturing Systems.
  • Data Analytics and Machine Learning in PHM and SHM.
  • Condition Monitoring and Fault Diagnosis.
  • The Optimization of Maintenance Strategies in Manufacturing.

Dr. Izaz Raouf
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. 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

  • prognostics and health management (PHM)
  • structural health management (SHM)
  • smart factory
  • application of AI
  • AI in mechanical engineering
  • maintenance
  • prognostics
  • SHM for composite materials
  • aerospace applications

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

Published Papers (1 paper)

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Research

15 pages, 4155 KiB  
Article
Structural Health Monitoring of Laminated Composites Using Lightweight Transfer Learning
by Muhammad Muzammil Azad, Izaz Raouf, Muhammad Sohail and Heung Soo Kim
Machines 2024, 12(9), 589; https://doi.org/10.3390/machines12090589 - 25 Aug 2024
Cited by 4 | Viewed by 1436
Abstract
Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent [...] Read more.
Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent and serious. Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). However, the direct application of these models is restricted by a lack of training data, necessitating the use of transfer learning. The commonly used transfer learning models are computationally expensive; therefore, the present research proposes lightweight transfer learning (LTL) models for the SHM of composites. The use of an EfficientNet–based LTL model only requires the fine-tuning of target vibration data rather than training from scratch. Wavelet-transformed vibrational data from various classes of composite laminates are utilized to confirm the effectiveness of the proposed method. Moreover, various assessment measures are applied to assess model performance on unseen test datasets. The outcomes of the validation show that the pre-trained EfficientNet–based LTL model could successfully perform the SHM of composite laminates, achieving high values regarding accuracy, precision, recall, and F1-score. Full article
(This article belongs to the Special Issue Prognostics and Health Management and Structural Health Management in Mechanical and Manufacturing Systems)
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