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Mathematics
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Artificial Intelligence for Fault Detection in Manufacturing
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Artificial Intelligence for Fault Detection in Manufacturing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1761

Special Issue Editor

Prof. Dr. Jinwoo Song

E-Mail Website
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Seoul, Republic of Korea
Interests: AI; fault detection; manufacturing; quality control

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue on "Artificial Intelligence for Fault Detection in Manufacturing: Addressing Data Imbalance for Reliable Quality Control", which will be published in Mathematics. This Special Issue focuses on the critical intersection of AI, manufacturing processes, and advanced mathematical techniques, highlighting the importance of addressing data imbalance to enhance the reliability and accuracy of quality control systems.

In recent years, the application of AI in manufacturing has become essential for maintaining high standards of quality control. However, a significant challenge in this domain is the imbalance of data, where abnormal or faulty data are scarce compared to normal operational data. This imbalance can hinder the effectiveness of AI-driven fault detection systems. Therefore, this Special Issue aims to gather cutting-edge research that leverages mathematical techniques—such as statistical modeling, data augmentation, and algorithmic optimization—combined with AI to overcome these challenges and improve manufacturing processes.

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

  • Mathematical optimization methods for enhancing AI accuracy;
  • AI-driven fault detection in manufacturing processes;
  • Addressing data imbalance through statistical modeling and data augmentation;
  • Synthetic data generation techniques for AI model training;
  • Machine learning algorithms tailored to manufacturing quality control;
  • Case studies on the practical implementation of AI in industrial settings;
  • Predictive modeling for fault detection and maintenance;
  • Optimal design using surrogate models.

Prof. Dr. Jinwoo Song
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. Mathematics 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 2600 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

  • artificial intelligence
  • fault detection
  • manufacturing
  • data imbalance
  • quality control
  • machine learning
  • statistical modeling
  • data augmentation
  • mathematical optimization
  • predictive maintenance

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Published Papers (3 papers)

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Research

16 pages, 1638 KiB  
Article
Computationally Efficient Design of 16-Poles and 24-Slots IPMSM for EV Traction Considering PWM-Induced Iron Loss Using Active Transfer Learning
by Soo-Hwan Park and Myung-Seop Lim
Mathematics 2025, 13(6), 915; https://doi.org/10.3390/math13060915 - 10 Mar 2025
Viewed by 197
Abstract
The efficiency of the traction motor is highly concerned with the PWM-induced iron loss, so the PWM-induced iron loss should be considered in designing the traction motor. However, analyzing the PWM-induced iron loss requires a high computational cost because the inverter-motor model should [...] Read more.
The efficiency of the traction motor is highly concerned with the PWM-induced iron loss, so the PWM-induced iron loss should be considered in designing the traction motor. However, analyzing the PWM-induced iron loss requires a high computational cost because the inverter-motor model should be included in the calculation process. In surrogate-based design optimization, collecting a large amount of data is essential. However, for PWM-induced iron loss, extremely small time steps are required to accurately capture high-frequency components, resulting in a significantly high computational cost for data acquisition and making the optimization process inefficient. From this point of view, we propose a computationally efficient design process for the traction motor considering the PWM-induced iron loss. By using the proposed method, it is possible to train the accurate surrogate model for predicting the PWM-induced iron loss with a small amount of PWM-induced iron loss using active transfer learning. After training the surrogate model, multi-objective optimization was conducted for designing a high efficiency 14.5 kW traction motor for personal mobility. In order to verify the design result, an optimized traction motor was fabricated, and experiments were conducted. As a result, the performance of the trained surrogate model was verified by measuring the no-load back electromotive force, PWM current, and main drive efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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18 pages, 3872 KiB  
Article
Multiscale 1D-CNN for Damage Severity Classification and Localization Based on Lamb Wave in Laminated Composites
by Olivier Munyaneza and Jung Woo Sohn
Mathematics 2025, 13(3), 398; https://doi.org/10.3390/math13030398 - 25 Jan 2025
Viewed by 648
Abstract
Lamb-wave-based structural health monitoring is widely employed to detect and localize damage in composite plates; however, interpreting Lamb wave signals remains challenging due to their dispersive characteristics. Although convolutional neural networks (CNNs) demonstrate a significant capability for pattern recognition within these signals relative [...] Read more.
Lamb-wave-based structural health monitoring is widely employed to detect and localize damage in composite plates; however, interpreting Lamb wave signals remains challenging due to their dispersive characteristics. Although convolutional neural networks (CNNs) demonstrate a significant capability for pattern recognition within these signals relative to other machine learning models, CNNs frequently encounter difficulties in capturing all the underlying patterns when the damage severity varies. To address this issue, we propose a multiscale, one-dimensional convolutional neural network (MS-1D-CNN) to assess the damage severity and localize damage in laminated plates. The MS-1D-CNN is capable of learning both low- and high-level features, enabling it to distinguish between minor and severe damage. The dataset was obtained experimentally via a sparse array of four lead zirconate titanates, with signals from twelve paths fused and downsampled before being input into the model. The efficiency of the model was evaluated using accuracy, precision, recall, and F1-score metrics for severity identification, along with the mean squared error, mean absolute error, and R2 for damage localization. The experimental results indicated that the proposed MS-1D-CNN outperformed support vector machine and artificial neural network models, achieving higher accuracy in both identifying damage severity and localizing damage with minimal error. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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19 pages, 5171 KiB  
Article
A Generalized Autonomous Power Plant Fault Detection Model Using Deep Feature Extraction and Ensemble Machine Learning
by Salman Khalid, Muhammad Muzammil Azad and Heung Soo Kim
Mathematics 2025, 13(3), 342; https://doi.org/10.3390/math13030342 - 22 Jan 2025
Viewed by 561
Abstract
Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders [...] Read more.
Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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