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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: 31 December 2025 | Viewed by 7175

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 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. 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 (6 papers)

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Research

31 pages, 3641 KB  
Article
Tool-Life Estimation Model in Milling Processes Using Multi-Head Cross-Covariance Attention Fusion-Based Dilated Dense Bi-Directional Gated Recurrent Unit
by Hisham Alkhalefah
Mathematics 2025, 13(23), 3798; https://doi.org/10.3390/math13233798 - 26 Nov 2025
Viewed by 190
Abstract
When performing the milling process, it is essential to consider the life estimation and availability of the milling tool to achieve a reliable and optimized result at a lower cost. It is necessary to monitor the tool’s condition during the milling process due [...] Read more.
When performing the milling process, it is essential to consider the life estimation and availability of the milling tool to achieve a reliable and optimized result at a lower cost. It is necessary to monitor the tool’s condition during the milling process due to its inherent wear nature. In earlier times, visual inspection was used to assess the condition of the milling tool, and it was considered a complex and specialized task. Due to this issue, the milling process requires further investigation. In the manufacturing and automation industry, deteriorated milling tools have led to several challenges, including a decline in product quality, reduced equipment utilization, and increased costs. The tool wear prediction is a challenging and complex task, as it includes several variables. The existing framework for tool condition monitoring, in terms of the degree, typically falls short in terms of real-time prediction and accuracy. Hence, in this research, a tool-life estimation model is developed to minimize unexpected failures during the milling process using deep learning techniques. Initially, the data are collected from benchmark sources. The statistical features, deep features via fuzzy autoencoders (FAEs), and t-Distributed Stochastic Neighbor Embedding (t-SNE)-based features are extracted from the input data to capture various information related to the machine. These features are passed to the proposed multi-head cross-covariance attention fusion-based dilated dense bi-directional gated recurrent unit (MCF-DD-BiGRU) for accurate prediction of tool life. The input features are fused using a multi-head cross-covariance attention mechanism to enhance the representation of interdependencies among features. The DBi-GRU network processes the fused features to improve the accuracy of tool-life prediction for milling machines. The prediction efficiency of the implemented model is compared with the existing models to ensure its effectiveness. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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45 pages, 12781 KB  
Article
Balanced Hoeffding Tree Forest (BHTF): A Novel Multi-Label Classification with Oversampling and Undersampling Techniques for Failure Mode Diagnosis in Predictive Maintenance
by Bita Ghasemkhani, Recep Alp Kut, Derya Birant and Reyat Yilmaz
Mathematics 2025, 13(18), 3019; https://doi.org/10.3390/math13183019 - 18 Sep 2025
Viewed by 828
Abstract
Predictive maintenance (PdM) is essential for reducing equipment downtime and enhancing operational efficiency. However, PdM datasets frequently suffer from significant class imbalance and are often limited to single-label classification, which fails to reflect the complexity of real-world industrial systems where multiple failure modes [...] Read more.
Predictive maintenance (PdM) is essential for reducing equipment downtime and enhancing operational efficiency. However, PdM datasets frequently suffer from significant class imbalance and are often limited to single-label classification, which fails to reflect the complexity of real-world industrial systems where multiple failure modes can occur simultaneously. As the main contribution, we propose the Balanced Hoeffding Tree Forest (BHTF)—a novel multi-label classification framework that combines oversampling and undersampling strategies to effectively mitigate data imbalance. BHTF leverages the binary relevance method to decompose the multi-label problem into multiple binary tasks and utilizes an ensemble of Hoeffding Trees to ensure scalability and adaptability to streaming data. In particular, BHTF unifies three learning paradigms—multi-label learning (MLL), ensemble learning (EL), and incremental learning (IL)—providing a comprehensive and scalable approach for predictive maintenance applications. The key contribution of the proposed method is that it incorporates a hybrid data preprocessing strategy, introducing a novel undersampling technique, named Proximity-Driven Undersampling (PDU), and combining it with the Synthetic Minority Oversampling Technique (SMOTE) to effectively deal with the class imbalance issue in highly skewed datasets. Experimental results on the benchmark AI4I 2020 dataset showed that BHTF achieved an average classification accuracy of 97.44%, outperformed by a margin of the state-of-the-art methods (88.94%) with an improvement of 11% on average. These findings highlight the potential of BHTF as a robust artificial intelligence-based solution for complex fault detection in manufacturing predictive maintenance applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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10 pages, 467 KB  
Article
An Analysis of Nonlinear Axisymmetric Structural Vibrations of Circular Plates with the Extended Rayleigh–Ritz Method
by Jie Han, Xianglin Gong, Chencheng Lian, Huimin Jing, Bin Huang, Yangyang Zhang and Ji Wang
Mathematics 2025, 13(8), 1356; https://doi.org/10.3390/math13081356 - 21 Apr 2025
Viewed by 785
Abstract
The nonlinear deformation and vibrations of elastic plates represent a fundamental problem in structural vibration analysis, frequently encountered in engineering applications and classical mathematical studies. In the field of studying the nonlinear phenomena of elastic plates, numerous methods and techniques have emerged to [...] Read more.
The nonlinear deformation and vibrations of elastic plates represent a fundamental problem in structural vibration analysis, frequently encountered in engineering applications and classical mathematical studies. In the field of studying the nonlinear phenomena of elastic plates, numerous methods and techniques have emerged to obtain approximate and exact solutions for nonlinear differential equations. A particularly powerful and flexible method, known as the extended Rayleigh–Ritz method (ERRM), has been proposed. In this approach, the temporal variable is introduced as an additional dimension in the formulation. Through expanded integration across both the physical domain and a vibration period, the temporal variable is eliminated. The ERRM builds on the traditional RRM that offers a straightforward, sophisticated, and highly effective way to approximate solutions for nonlinear vibration and deformation issues in the realm of structural dynamics and vibration. In the case of circular plates, the method incorporates the linear displacement function along with high-frequency terms. As a result, it can accurately determine the nonlinear axisymmetric vibration frequencies of circular plates. For scenarios involving smaller deformations, its accuracy is on par with other approximate solution methods. This approach provides a valuable and novel procedure for the nonlinear analysis of circular structural vibrations. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection in Manufacturing)
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16 pages, 1638 KB  
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 1335
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 KB  
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
Cited by 2 | Viewed by 1825
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 KB  
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
Cited by 3 | Viewed by 1538
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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