Machines

23 pages, 5602 KB

Open AccessArticle

Transient Analysis of Vortex-Induced Pressure Pulsations in a Vertical Axial Pump with Bidirectional Flow Passages Under Stall Conditions

by Fan Meng, Haoxuan Tang, Yanjun Li, Jiaxing Lu, Qixiang Hu and Mingming Ge

Machines 2026, 14(1), 34; https://doi.org/10.3390/machines14010034 (registering DOI) - 25 Dec 2025

Abstract

Vertical axial-flow pumps with bidirectional passages are widely used in applications requiring flow reversal. However, their unique inlet geometry often leads to asymmetric impeller inflow conditions. This study investigates the internal flow behavior and pressure pulsation characteristics of a vertical bidirectional axial-flow pump [...] Read more.

Vertical axial-flow pumps with bidirectional passages are widely used in applications requiring flow reversal. However, their unique inlet geometry often leads to asymmetric impeller inflow conditions. This study investigates the internal flow behavior and pressure pulsation characteristics of a vertical bidirectional axial-flow pump under design, critical stall, and deep stall conditions using unsteady Reynolds-averaged Navier–Stokes simulations combined with Fast Fourier Transform and wavelet analysis. Results show that the pump reaches peak efficiency at the design point, with critical and deep stall occurring at 0.6 Q_des and 0.5 Q_des, respectively. The head at the deep stall condition shows a further drop of 7.51% compared to the critical stall condition. This progressive performance degradation is attributed to vortex-induced blockage: it initiates with the intensification of the tip leakage vortex and evolves into large-scale separation vortices covering the suction surface under deep stall—a mechanism distinctly influenced by the bidirectional inlet’s stagnant water zone. Inlet asymmetry, reflected by a normalized velocity coefficient (V_n) below 0.6 in the stagnant water zone under design flow, is partially mitigated during stall due to flow confinement. Pressure pulsations at the blade leading edge are dominated by the blade passing frequency (BPF), with amplitudes under critical stall about 3.2 times those at design conditions. At the impeller outlet, critical stall produces a mixed dominant frequency (shaft frequency and BPF), whereas deep stall yields the highest pulsation amplitude (BPF ≈ 4.8 × the design value) resulting from extreme passage blockage. These findings clarify how bidirectional-inlet-induced vortices modulate stall progression and provide theoretical guidance for enhancing the operational stability of such pumps under off-design conditions. Full article

(This article belongs to the Section Turbomachinery)

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33 pages, 757 KB

Open AccessReview

Evolution and Emerging Trends in Intelligent Wheelchair Control: A Comprehensive Review

by Atulan Gupta, Kanan Roy Chowdhury, Nusrat Farheen and Marco P. Schoen

Machines 2026, 14(1), 33; https://doi.org/10.3390/machines14010033 (registering DOI) - 25 Dec 2025

Abstract

As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and the emerging trends in intelligent wheelchair control. A [...] Read more.

As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and the emerging trends in intelligent wheelchair control. A specific focus is provided on classifying and comparing model-driven and data-driven control methodologies. In this review, findings from a range of past contributions are examined, including conventional control theories, rule-based systems, and modern data-driven approaches that include supervised, unsupervised, and reinforcement learning control algorithms. The analysis indicates that while model-driven methods offer interpretability, data-driven techniques—in particular those leveraging machine learning—provide for a superior adaptability for navigating complex and dynamic environments. We further highlight key supporting systems found in sensors, actuators, and human-machine interfaces. Additionally, the important functionalities such as autonomous navigation and obstacle avoidance methods are identified. Our findings point to some future objectives that need to be addressed. For example, energy efficiency, robustness in unpredictable settings, computational requirements, and associated demands when utilizing data-driven methods. One of the highlighted fields of study in this work is the integration of reinforcement learning and sensor fusion, which may hold some promising results for future wheelchair technologies. Full article

(This article belongs to the Section Automation and Control Systems)

25 pages, 7587 KB

Open AccessArticle

LiMS-MFormer: A Lightweight Multi-Scale and Multi-Dimensional Attention Transformer for Robust Rolling Bearing Fault Diagnosis Under Complex Conditions

by Haixiao Cao, Chuanlong Ding, Yonghong Zhang and Liang Jiang

Machines 2026, 14(1), 32; https://doi.org/10.3390/machines14010032 (registering DOI) - 25 Dec 2025

Abstract

Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, [...] Read more.

Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, and limited suitability for lightweight deployment. This study proposes a Lightweight Multi-Scale Multi-Dimensional Self-Attention Transformer (LiMS-MFormer)—an end-to-end lightweight fault diagnosis framework integrating multi-scale feature extraction and multi-dimensional attention. The model integrates lightweight multi-scale convolutional feature extraction, hierarchical feature fusion, and a multi-dimensional self-attention mechanism to balance feature expressiveness with computational efficiency. Specifically, the front end employs Ghost convolution and enhanced residual structures for efficient multi-scale feature extraction. The middle layers perform cross-scale concatenation and fusion to enrich contextual representations. The back end introduces a lightweight temporal-channel-spatial attention module for global modeling and focuses on key patterns. Experiments on the Paderborn University (PU) dataset and the University of Ottawa bearing vibration dataset (Ottawa dataset) show that LiMS-MFormer achieves an accuracy of 96.68% on the small-sample PU dataset while maintaining minimal parameters (0.07 M) and low computational cost (13.55 M FLOPs). Moreover, under complex noisy conditions, the proposed model demonstrates strong fault diagnosis capability. On the University of Ottawa dataset, LiMS-MFormer consistently outperforms several state-of-the-art lightweight models, exhibiting superior accuracy, robustness, and generalization in challenging diagnostic tasks. Full article

(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)

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19 pages, 5071 KB

Open AccessReview

Research Progress on Optical Fiber Sensing Based Health Monitoring Technology for Aerospace Composite Structures

by Xiang Zhou, Xiaolei Zhang, Jianxin He, Chao Yin and Xing Shen

Machines 2026, 14(1), 31; https://doi.org/10.3390/machines14010031 (registering DOI) - 25 Dec 2025

Abstract

The large-scale deployment of aerospace composite structures has become a defining trend in modern aeronautics; however, hidden damage is difficult to detect over the full life cycle with conventional non-destructive inspection. This creates an urgent demand for on-line, high-fidelity structural health monitoring (SHM) [...] Read more.

The large-scale deployment of aerospace composite structures has become a defining trend in modern aeronautics; however, hidden damage is difficult to detect over the full life cycle with conventional non-destructive inspection. This creates an urgent demand for on-line, high-fidelity structural health monitoring (SHM) technology. Optical-fiber sensors—featuring minimal mass, micron-scale diameter, immunity to electromagnetic interference and the ability to be co-cured into composite laminates for distributed measurement—are widely regarded as the key enabling technology. This paper presents a comprehensive review of recent advances and engineering applications of optical fiber sensing. Emphasis is placed on its engineering applications covering wing strain mapping, landing-gear load tracking, fuselage deformation localization, and cure-process monitoring and low-velocity impact damage identification of composite materials. Emerging intelligent assessment methodologies are examined. Finally, the development trends of optical fiber sensing technology are prospected, offering a reference framework for future theoretical innovation and engineering deployment of aerospace composite SHM technology. Full article

(This article belongs to the Special Issue Smart Structures and Applications in Aerospace Engineering)

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20 pages, 6695 KB

Open AccessArticle

Research on the Nonlinear Stiffness Characteristics of Spline Coupling Under Multiple Contact Surfaces

by Chongbei Huang and Yinli Feng

Machines 2026, 14(1), 30; https://doi.org/10.3390/machines14010030 (registering DOI) - 25 Dec 2025

Abstract

Spline couplings endure various external loads during operation, which induce complex variations in contact surfaces and significantly affect their stiffness behavior. This research focuses on a specific spline coupling to analyze its nonlinear stiffness characteristics and underlying mechanisms. Employing the finite element method, [...] Read more.

Spline couplings endure various external loads during operation, which induce complex variations in contact surfaces and significantly affect their stiffness behavior. This research focuses on a specific spline coupling to analyze its nonlinear stiffness characteristics and underlying mechanisms. Employing the finite element method, a simulation model is developed to investigate the nonlinear load-dependent stiffness changes under multiple contact surfaces. The study investigates the nonlinear stiffness characteristics of the spline coupling through sensitivity analysis of stiffness to contact surfaces and contact state changes, and reveals the patterns of contact state evolution. The analysis indicates that as external loads increase, the spline coupling stiffness decreases significantly. Variations in the contact states of each surface induce nonlinear stiffness variations, with centering surface B (the cylindrical centering surface on the right side of the spline coupling) exhibiting the most substantial influence on nonlinear stiffness changes. Furthermore, the effects of centering surface clearance, applied torque, and friction coefficients across contact surfaces on the spline coupling stiffness are examined. Stiffness increases as clearance decreases and torque increases, while friction coefficients exhibit a negligible impact on stiffness performance. Full article

(This article belongs to the Section Turbomachinery)

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12 pages, 3243 KB

Open AccessCommunication

Adherend-Limited Failure in LCD Print-to-Bond Woven Fabric-Photopolymer Joints: A Process Efficiency Communication

by Ivan Grgić, Mirko Karakašić, Pejo Konjatić and Vivek Kumar Tiwary

Machines 2026, 14(1), 29; https://doi.org/10.3390/machines14010029 - 24 Dec 2025

Abstract

Additive manufacturing via LCD vat photopolymerisation enables direct bonding of photopolymer to textile substrates, but optimal processing parameters remain unclear. A 3 × 3 factorial design investigated the effects of layer thickness (0.01, 0.025, 0.05 mm) and UV exposure time (40, 80, 120 [...] Read more.

Additive manufacturing via LCD vat photopolymerisation enables direct bonding of photopolymer to textile substrates, but optimal processing parameters remain unclear. A 3 × 3 factorial design investigated the effects of layer thickness (0.01, 0.025, 0.05 mm) and UV exposure time (40, 80, 120 s) on the single-lap shear strength of woven fabric-photopolymer joints (65% polyester/35% cotton) using a novel pause-and-bond methodology, following the EN ISO 4587:2003 standard. Five replicate specimens per condition yielded 45 samples for mechanical testing. All specimens (45/45) exhibited adherend-limited failure within the textile substrate rather than at the polymer-textile interface, yielding consistent shear strengths of 1.38 ± 0.04 MPa (range: 1.30–1.45 MPa). Two-way ANOVA revealed no significant parametric effects (p > 0.05), indicating that interfacial bond strength consistently exceeded textile cohesive strength across all parameter combinations. The minimum resource-efficient condition (0.01 mm/40 s) achieves equivalent performance to higher-parameter combinations, enabling substantial process optimisation for textile-integrated photopolymer sandwich structures while reducing material and processing time requirements. Full article

(This article belongs to the Special Issue Advances in 3D Printing Technologies: Materials, Processes, and Applications)

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15 pages, 3718 KB

Open AccessArticle

Pedestrian Protection Performance Prediction Based on Deep Learning

by Hongbin Tang, Zheng Dou, Xuesong Wang, Zehui Huang and Zihang Li

Machines 2026, 14(1), 28; https://doi.org/10.3390/machines14010028 - 24 Dec 2025

Abstract

In order to maintain pedestrian safety in vehicle collisions and enhance collision safety, this paper proposes a rapid prediction method of head injuries for pedestrian protection based on deep learning, which could be used to design and optimize pedestrian protection performance during the [...] Read more.

In order to maintain pedestrian safety in vehicle collisions and enhance collision safety, this paper proposes a rapid prediction method of head injuries for pedestrian protection based on deep learning, which could be used to design and optimize pedestrian protection performance during the vehicle design stage. However, traditional finite element simulation methods involve a large computational effort and long calculation time, and multiple computations are required to obtain the corresponding pedestrian head injury results for engine hood structural optimization. Therefore, to accelerate the design process and save time costs, this paper proposes a deep learning-based method for the rapid prediction of pedestrian head injuries. Compared with traditional finite element simulation techniques, this method will greatly improve the efficiency of obtaining head injury results, and its core lies in establishing a prediction model for pedestrian head injury results. The sample data for establishing the prediction model is defined initially, in which the head injury criterion (HIC) and vehicle structure serve as the output and input of the prediction model, respectively. The voxelization method is used to digitally express the car body structure. Convolutional neural networks (CNNs) such as ResNet50, MobileNet, SqueezeNet, and ShuffleNet are used to train the model. After adjusting the dataset and model hyperparameters, the prediction model with the smallest error is obtained. The cross-validation method was used to verify the robustness and generalization ability of the model. The average error rate of the obtained prediction model for predicting head injuries was 14.28%, which proved the accuracy and applicability of the prediction model. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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25 pages, 4196 KB

Open AccessArticle

Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control

by Mingxin Li, Hui Li, Yunan Yao, Yulei Zhu, Hailong Weng, Huabiao Jin and Taiwei Yang

Machines 2026, 14(1), 27; https://doi.org/10.3390/machines14010027 - 24 Dec 2025

Abstract

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed [...] Read more.

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed model predictive control (DMPC). The RL agent dynamically adjusts the optimization weights of the DMPC to adapt to the vehicle’s real-time environment, while the DMPC enables decentralized path planning and collision avoidance. The system leverages multi-source sensor fusion, including GNSS, UWB, IMU, LiDAR, and stereo cameras, to provide accurate state estimations of vehicles. Simulation results demonstrate that the proposed RL-DMPC approach outperforms traditional centralized control strategies in terms of tracking accuracy, collision avoidance, and safety margins. Furthermore, the proposed method significantly improves control smoothness compared to rule-based strategies. This framework is particularly effective in dynamic and constrained industrial settings, offering a robust solution for multi-vehicle coordination with minimal communication delays. The study highlights the potential of combining RL with DMPC to achieve real-time, scalable, and adaptive solutions for autonomous logistics. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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37 pages, 1401 KB

Open AccessArticle

An AI Digital Platform for Fault Diagnosis and RUL Estimation in Drivetrain Systems Under Varying Operating Conditions

by Dimitrios M. Bourdalos, Xenofon D. Konstantinou, Josef Koutsoupakis, Ilias A. Iliopoulos, Kyriakos Kritikakos, George Karyofyllas, Panayotis E. Spiliotopoulos, Ioannis E. Saramantas, John S. Sakellariou, Dimitrios Giagopoulos, Spilios D. Fassois, Panagiotis Seventekidis and Sotirios Natsiavas

Machines 2026, 14(1), 26; https://doi.org/10.3390/machines14010026 - 24 Dec 2025

Abstract

Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using [...] Read more.

Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article

(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)

26 pages, 900 KB

Open AccessArticle

Quality Management for AI-Generated Self-Adaptive Resource Controllers

by Claus Pahl, Hamid R. Barzegar and Nabil El Ioini

Machines 2026, 14(1), 25; https://doi.org/10.3390/machines14010025 - 24 Dec 2025

Abstract

Many complex systems requires the use of controllers to allow an automated, self-adaptive management of components and resources. Controllers are software components that observe a system, analyse its quality, and recommend and enact decisions to maintain or improve quality. While controllers have been [...] Read more.

Many complex systems requires the use of controllers to allow an automated, self-adaptive management of components and resources. Controllers are software components that observe a system, analyse its quality, and recommend and enact decisions to maintain or improve quality. While controllers have been for many years, recently Artificial Intelligence (AI) techniques such as Machine Learning (ML) and specifically reinforcement learning (RL) are used to construct these controllers, causing uncertainties about the quality of them due to their construction. We investigate quality metrics for RL-constructed software-based controllers that allow for their continuous quality control, which is particularly motivated by increasing automation and also the usage of artificial intelligence and control theoretic solutions for controller construction and operation. We introduce self-adaptation and control principles and define a quality-oriented controller reference architecture for controllers for self-adaptive systems. This forms the basis for the central contribution, a quality analysis metrics framework for controllers themselves. Full article

(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)

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30 pages, 3944 KB

Open AccessArticle

An Integrated Control Strategy for Trajectory Tracking of a Crane-Suspended Load

by Diankai Kong, Fenglin Yao, Chao Hu, Yuyan Guo and Wei Ye

Machines 2026, 14(1), 24; https://doi.org/10.3390/machines14010024 - 24 Dec 2025

Abstract

With the advancement of intelligent technologies, industrial production systems are being profoundly transformed by artificial intelligence algorithms. To address persistent challenges, such as cargo swing and low operational efficiency during the lifting processes of all-terrain cranes, this research investigates an intelligent control algorithm [...] Read more.

With the advancement of intelligent technologies, industrial production systems are being profoundly transformed by artificial intelligence algorithms. To address persistent challenges, such as cargo swing and low operational efficiency during the lifting processes of all-terrain cranes, this research investigates an intelligent control algorithm designed for swing suppression and high-stability payload trajectory control. Firstly, a nonlinear dynamic model of the crane system was derived using the Euler–Lagrange formulation based on a simplified three-dimensional representation. A linear time-varying model predictive control (LTV-MPC) strategy was then designed to incorporate real-time feedback during luffing and slewing motions to monitor the payload’s swing state. On this basis, the controller predicts the desired trajectory and applies negative feedback to adjust the control input, thereby steering the system toward the optimal trajectory and aligning it with the target path. Secondly, a comparative analysis was conducted among four scenarios: the natural swing state of the payload and three control strategies—LTV-MPC, sliding mode control (SMC), and PID control—under both single-input and dual-input conditions. Finally, an experimental platform was established, employing the YOLOv12 algorithm for real-time detection and trajectory tracking of the suspended payload. The experimental results validate the effectiveness of LTV-MPC in suppressing cargo swing. Under single-input control, LTV-MPC achieved the best performance in both stabilization time (3.05 s for luffing condition one and 1.15 s for luffing condition two) and steady-state error (0.003–0.007°). The swing angle, θ₁, was reduced by 91.9%, 54.2%, and 59.3% compared to the natural swing state, SMC, and PID, respectively. In dual-input control, LTV-MPC attained a steady-state error of only 0.0008° under “luffing condition two,” while during slewing operations, it also outperformed SMC and PID in both settling time (26.05 s) and precision (0.008°). Full article

(This article belongs to the Section Machine Design and Theory)

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19 pages, 6380 KB

Open AccessArticle

Design and Analysis of an Anti-Collision Spacer Ring and Installation Robot for Overhead Transmission Lines

by Tianlei Wang, Huize Lian and Tianhui Cheng

Machines 2026, 14(1), 23; https://doi.org/10.3390/machines14010023 - 24 Dec 2025

Abstract

Overhead transmission lines often suffer from mutual collisions between adjacent conductors in windy weather, which can cause power failures to villages. To solve this problem, this paper introduces a spacer ring and a teleoperated robot for the installation and retrieval of the ring. [...] Read more.

Overhead transmission lines often suffer from mutual collisions between adjacent conductors in windy weather, which can cause power failures to villages. To solve this problem, this paper introduces a spacer ring and a teleoperated robot for the installation and retrieval of the ring. The spacer ring and robot address the installation challenges of the anti-collision devices and enhance transmission line maintenance. Fixed by the locking mechanism, the spacer ring can isolate adjacent conductors to avoid collisions. The structure and working principle of the spacer ring and installation robot are introduced. Static analysis and finite element analysis (FEA) are conducted to analyze the output force of the locking mechanism, which is then validated through experiments. Experimental results show that the locking mechanism can generate a strong output force of up to 2000 N with about 6.0 N·m of input torque, providing a secure installation for the spacer ring. Diverse installation tests have validated the robot’s capability for live-line operations on transmission lines. Field tests indicate that the installation robot can travel at 0.3 m/s on a 15° slope and successfully install the spacer rings. Full article

(This article belongs to the Section Machine Design and Theory)

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31 pages, 4638 KB

Open AccessArticle

Improvement in DFIG-Based Wind Energy Conversion System LVRT Capability in Compliance with Algerian Grid Code

by Brahim Djidel, Lakhdar Mokrani, Abdellah Kouzou, Mohamed Machmoum, Jose Rodriguez and Mohamed Abdelrahem

Machines 2026, 14(1), 22; https://doi.org/10.3390/machines14010022 - 23 Dec 2025

Abstract

During voltage dips, wind turbines must remain connected to the electrical grid and contribute to voltage stabilization. This study analyzes the impact of voltage dips arising from grid faults on Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECSs). This paper [...] Read more.

During voltage dips, wind turbines must remain connected to the electrical grid and contribute to voltage stabilization. This study analyzes the impact of voltage dips arising from grid faults on Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECSs). This paper presents a review of the technical regulations for integrating the Algerian electricity grid with the Low Voltage Ride Through (LVRT) system, along with specific requirements for renewable power generation installations. Additionally, the modeling and control strategy of DFIG based WECS has been outlined. Voltage dips can induce excessive currents that threaten the DFIG rotor and may cause harmful peak oscillations in the DC-link voltage, and can lead to turbine speed increase due to the sudden imbalance between the mechanical input torque and the reduced electromagnetic torque. To counter this, a modified vector control and crowbar protection mechanism were integrated. Its role is to mitigate these risks, thereby ensuring the system remains stable and operational through grid faults. The proposed system successfully meets the stringent Algerian LVRT requirements, with voltage dipping to zero for 0.3 s and recovering gradually. Simulations confirm that rotor and stator currents remain within safe limits (peak rotor current at 0.93

p u

, and peak stator current at 1.36

p u

). The DC-link voltage, despite a transient rise due to the continued power conversion from the rotor-side converter during the grid fault, was effectively stabilized and maintained within safe operating margins (with less than 14% overshoot). This stability was achieved as the crowbar ensured power balance by managing active and reactive power. Notably, the turbine rotor speed demonstrated stability, peaking at 1.28

p u

within mechanical limits. Full article

(This article belongs to the Special Issue Advances in Power Electronics for Electromechanical Energy Conversion and Drive Systems)

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24 pages, 5595 KB

Open AccessArticle

Online End Deformation Calculation Method for Mill Relining Manipulator Based on Structural Decomposition and Kolmogorov-Arnold Network

by Mingyuan Wang, Yujun Xue, Jishun Li, Shuai Li and Yunhua Bai

Machines 2026, 14(1), 21; https://doi.org/10.3390/machines14010021 - 23 Dec 2025

Abstract

Due to the large mass, high end load, and long action distance of a mill relining manipulator, gravity effects inevitably lead to a reduction in end effector positioning accuracy. To solve this problem, an online calculation method is proposed to realize real-time end [...] Read more.

Due to the large mass, high end load, and long action distance of a mill relining manipulator, gravity effects inevitably lead to a reduction in end effector positioning accuracy. To solve this problem, an online calculation method is proposed to realize real-time end effector deformation prediction. First, a manipulator is simplified into two cantilever beams: the upper arm and the forearm. Second, a reaction force and moment transformation model is established based on the coupling relationship between the forearm and upper arm. Third, finite element (FE) static analysis and simulation are carried out to obtain the end deformation. A total of 3528 discrete joint configurations are selected to cover the entire joint space, and their corresponding FE solutions are used to establish the end deformation offline dataset. Finally, an online deformation calculation algorithm based on Kolmogorov–Arnold networks (KANs) is developed to predict end deformation in any working condition. Visualization analysis and validation experiments are conducted and demonstrate the superiority of the proposed method in reducing gravity effects and improving computational efficiency. In summary, the proposed method provides support for end position compensation, especially for heavy-duty manipulators. Full article

(This article belongs to the Special Issue The Kinematics and Dynamics of Mechanisms and Robots)

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27 pages, 3196 KB

Open AccessArticle

Reliability-Based Robust Design Optimization Using Data-Driven Polynomial Chaos Expansion

by Zhaowang Li, Zhaozhan Li, Jufang Jia and Xiangdong He

Machines 2026, 14(1), 20; https://doi.org/10.3390/machines14010020 - 23 Dec 2025

Abstract

As the complexity of modern engineering systems continues to increase, traditional reliability analysis methods still face challenges regarding computational efficiency and reliability in scenarios where the distribution information of random variables is incomplete and samples are sparse. Therefore, this study develops a data-driven [...] Read more.

As the complexity of modern engineering systems continues to increase, traditional reliability analysis methods still face challenges regarding computational efficiency and reliability in scenarios where the distribution information of random variables is incomplete and samples are sparse. Therefore, this study develops a data-driven polynomial chaos expansion (DD-PCE) model for scenarios with limited samples and applies it to reliability-based robust design optimization (RBRDO). The model directly constructs orthogonal polynomial basis functions from input data by matching statistical moments, thereby avoiding the need for original data or complete statistical information as required by traditional PCE methods. To address the statistical moment estimation bias caused by sparse samples, kernel density estimation (KDE) is employed to augment the data derived from limited samples. Furthermore, to enhance computational efficiency, after determining the DD-PCE coefficients, the first four moments of the DD-PCE are obtained analytically, and reliability is computed based on the maximum entropy principle (MEP), thereby eliminating the additional step of solving reliability as required by traditional PCE methods. The proposed approach is validated through a mechanical structure and five mathematical functions, with RBRDO studies conducted on three typical structures and one practical engineering case. The results demonstrate that, while ensuring computational accuracy, this method saves approximately 90% of the time compared to the Monte Carlo simulation (MCS) method, significantly improving computational efficiency. Full article

(This article belongs to the Section Machine Design and Theory)

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51 pages, 16272 KB

Open AccessReview

A Review on In-Situ Monitoring in Wire Arc Additive Manufacturing: Technologies, Applications, Challenges, and Needs

by Mohammad Arjomandi, Jackson Motley, Quang Ngo, Yoosuf Anees, Muhammad Ayaan Afzal and Tuhin Mukherjee

Machines 2026, 14(1), 19; https://doi.org/10.3390/machines14010019 - 22 Dec 2025

Abstract

Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate [...] Read more.

Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation. Full article

(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)

2 pages, 432 KB

Open AccessCorrection

Correction: Liu et al. A Semi-Analytical Loaded Contact Model and Load Tooth Contact Analysis Approach of Ease-Off Spiral Bevel Gears. Machines 2024, 12, 623

by Yuhui Liu, Liping Chen, Xian Mao and Duansen Shangguan

Machines 2026, 14(1), 18; https://doi.org/10.3390/machines14010018 - 22 Dec 2025

Abstract

There is an error in the original publication [...] Full article

(This article belongs to the Section Electrical Machines and Drives)

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22 pages, 5185 KB

Open AccessArticle

AI-Based Predictive Maintenance for Rotor Crack Fault Diagnosis for Variable-Speed Machines Using Transfer Learning

by Sudhar Rajagopalan, Seemu Sharma and Ashish Purohit

Machines 2026, 14(1), 17; https://doi.org/10.3390/machines14010017 - 21 Dec 2025

Abstract

Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the [...] Read more.

Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the testing speed differs from the training speed. This research addresses significant performance loss issues using convolutional neural network (CNN)-based transfer learning models. The main causes of performance loss are domain shift, overfitting, data class imbalance, low fault data availability, and biassed prediction. All the above difficult issues make CNN-based fault prediction systems function badly under varying operating conditions. The proposed methodology addresses all domain adaptation challenges. The proposed methodology was tested by collecting vibration data from an experimental rotor system under varied operating conditions. The proposed methodology outperforms classical machine learning (ML) and deep learning (DL) models, overcoming the overfitting issue with optimised hyperparameters, achieving a prediction accuracy of 99.5%. Under varying operating conditions, it outperforms with a prediction accuracy of 93.2%, and in the ‘data class imbalanced’ scenario, the maximal transfer learning capability achieved was 84.4% with the highest F1-Score. Thus, CNN-based transfer learning enables industrial variable speed machines diagnose rotor crack flaws better than ML and DL models. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)

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21 pages, 1718 KB

Open AccessArticle

Innovative Stability Design for a Specialized Handling Trolley for Sampling Devices

by Mária Vargovská, Roman Čierťažský and Elena Pivarčiová

Machines 2026, 14(1), 16; https://doi.org/10.3390/machines14010016 - 21 Dec 2025

Abstract

This article presents an analytical and simulation analysis of the stability of an innovative handling trolley. The analysis demonstrated that the loaded trolley (100 kg load) requires a critical tipping force F_crit of 502.24 N and a work W of 279.05 J. [...] Read more.

This article presents an analytical and simulation analysis of the stability of an innovative handling trolley. The analysis demonstrated that the loaded trolley (100 kg load) requires a critical tipping force F_crit of 502.24 N and a work W of 279.05 J. A comparative analysis confirmed a 128% higher force stability for the proposed solution compared to a standard model F_crit = 220 N. Following the structural design, a prototype was created and tested directly at the workplace for which it was designed; in addition to load tests, which it passed without issue, it was necessary to verify its stability. This step was approached from both a theoretical and practical standpoint. Given the need for special clamping of the transported material, a test was first performed on the empty handling trolley, and subsequently, the trolley was verified with the material clamped. This procedure was applied to the theoretical mathematical analytical solution, the simulation, and the practical test. This process required full consideration, given the manner of clamping, the robust and heavy nature of the transported material, and its operation by a single operator. In the practical test, pressure was applied to the trolley, both without load and with load, which verified and confirmed its stability in both longitudinal and transverse directions. The conclusions define that the trolley’s structure was even more stable after adding the load (handling material). A prototype was created and tested directly at the workplace. Practical stability tests were conducted by applying lateral pressure to both empty and loaded configurations, confirming stability in longitudinal and transverse directions. Formal tilt-table testing according to EN 1757 and ISO 22915 standards is planned for final certification. Full article

(This article belongs to the Special Issue Mechanics and Industrial Automation)

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