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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: 31 July 2026 | Viewed by 6522

Special Issue Editor

Dr. Izaz Raouf

E-Mail Website
Guest Editor
Department of Aerospace and Mechanical Engineering, Case Western Reserve University, Cleveland, OH 44106-7027, USA
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 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. 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 (4 papers)

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Research

28 pages, 7980 KB  
Article
Smart Predictive Maintenance: A TCN-Based System for Early Fault Detection in Industrial Machinery
by Abuzar Khan, Ahmad Junaid, Muhammad Farooq Siddique, Abid Iqbal, Husam S. Samkari, Mohammed F. Allehyani and Ghassan Husnain
Machines 2026, 14(2), 164; https://doi.org/10.3390/machines14020164 - 1 Feb 2026
Viewed by 858
Abstract
Modern factories still struggle with unexpected machine failures because traditional maintenance systems depend on fixed rules and threshold-based alerts. These older approaches often overlook subtle or complex patterns in multimodal sensor data, causing them to miss early signs of wear and leading to [...] Read more.
Modern factories still struggle with unexpected machine failures because traditional maintenance systems depend on fixed rules and threshold-based alerts. These older approaches often overlook subtle or complex patterns in multimodal sensor data, causing them to miss early signs of wear and leading to late or incorrect maintenance decisions. As a result, production can slow down, costs increase and equipment reliability suffers. To address this challenge, this study introduces a smart and interpretable fault diagnosis and predictive maintenance framework designed to detect wear, degradation and potential failures before they disrupt operations. The proposed framework integrates multiscale feature extraction, multimodal sensor fusion and cross-sensor correlation analysis with advanced temporal modeling using a Temporal Convolutional Network (TCN). By jointly performing tool-health classification and Remaining Useful Life (RUL) estimation, the framework provides a comprehensive assessment of machine condition. When evaluated on the NASA Ames milling dataset, the model achieved an overall accuracy of 86%, correctly classifying healthy and failed tools in more than 88% of cases and worn tools in over 75%, demonstrating consistent performance across different stages of tool wear. Explainable artificial intelligence (XAI) techniques, including attention-based visualizations and SHAP-based feature attribution, reveal that electrical and vibration signals are the most influential early indicators of tool degradation. The proposed framework exhibits low computational latency and minimal memory requirements, making it suitable for real-time fault diagnosis and deployment on industrial edge devices. Overall, the framework balances predictive accuracy, interpretability and practical applicability, enabling proactive and reliable maintenance decisions that enhance machine uptime and support efficient smart manufacturing operations. 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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22 pages, 4161 KB  
Article
Hybrid One-Dimensional Convolutional Neural Network—Recurrent Neural Network Model for Reconstructing Missing Data in Structural Health Monitoring Systems
by Nguyen Thi Thu Nga, Jose C. Matos and Son Dang Ngoc
Machines 2025, 13(12), 1101; https://doi.org/10.3390/machines13121101 - 27 Nov 2025
Viewed by 826
Abstract
Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and [...] Read more.
Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s2 and R2 = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s2 and R2 = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications. 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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14 pages, 3542 KB  
Article
Study on Angular Velocity Measurement for Characterizing Viscous Resistance in a Ball Bearing
by Kyungmok Kim
Machines 2025, 13(7), 578; https://doi.org/10.3390/machines13070578 - 3 Jul 2025
Cited by 4 | Viewed by 1068
Abstract
This article describes a machine vision-based method for measuring the angular velocity of a rotating disk to characterize the viscous resistance of a ball bearing. A bright marker was attached to a disk connected to a shaft supported by two ball bearings. Rotation [...] Read more.
This article describes a machine vision-based method for measuring the angular velocity of a rotating disk to characterize the viscous resistance of a ball bearing. A bright marker was attached to a disk connected to a shaft supported by two ball bearings. Rotation of the marker was recorded with a digital camera. A simple algorithm was developed to track the trajectory of the marker and calculate angular displacement of the disk. For accurate detection of the rotating marker, the algorithm employed Multi-Otsu thresholding and the Least Squares Method (LSM). Verification of the proposed method was carried out through a direct comparison between the predicted rotational speeds and measured ones by a commercial tachometer. It was demonstrated that the percentage error of the proposed method was less than 1.75 percent. The evolution of angular velocity after motor power-off was measured and found to follow an exponential decay law. The exponent was found to remain consistent regardless of the induced rotational speed. This proposed measurement method will offer a simple and accurate non-contact solution for monitoring angular velocity and characterizing the resistance of a bearing. 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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15 pages, 4155 KB  
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 5 | Viewed by 2556
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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