Machines

42 pages, 16440 KB

Open AccessReview

Conducted Electromagnetic Interference Mechanisms and Mitigation Techniques in SiC Electric Vehicle Traction Inverters: A Review

by Lin Chen, Qinjie Hu, Tianyang Wang, Jiawei Qin, Kanlun Tan, Li Yang, Qi Li and Dafang Wang

Machines 2026, 14(9), 1068; https://doi.org/10.3390/machines14091068 (registering DOI) - 17 Sep 2026

Abstract

Due to the higher power density of silicon carbide (SiC) inverters in electric vehicles (EVs), effectively managing conducted electromagnetic interference (EMI) has become a vital aspect of inverter design. The complex power topology of SiC inverters increases the complexity of different types, phenomena, [...] Read more.

Due to the higher power density of silicon carbide (SiC) inverters in electric vehicles (EVs), effectively managing conducted electromagnetic interference (EMI) has become a vital aspect of inverter design. The complex power topology of SiC inverters increases the complexity of different types, phenomena, and mechanisms of conducted EMI, making the selection of appropriate suppression methods more challenging. Many studies have examined the mechanisms of conducted EMI and their suppression techniques. However, the fast switching transients of SiC devices can affect an automotive traction inverter at multiple physical levels, ranging from the gate-drive circuit and isolation interface to the external power terminals. These phenomena are closely related through their common switching excitation and parasitic coupling networks, but they should not all be interpreted as equivalent conducted-emission phenomena. To provide a structured engineering perspective, this review organizes the relevant disturbances using a source–path–victim framework and examines three representative paths: gate-loop crosstalk, common-mode (CM) coupling across the isolated gate-drive interface, and system-level CM/DM-conducted emissions. The corresponding mitigation techniques and their applicability to EV traction inverters are subsequently reviewed. Full article

(This article belongs to the Special Issue Advanced Power Electronic Technologies in Electric Drive Systems, 2nd Edition)

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26 pages, 11005 KB

Open AccessArticle

A Computationally Efficient Pseudo-2D PEM Fuel Cell Model for Studying Humidity Distribution Without External Humidification: The Role of Anode Recirculation

by Noé Labeyrie, Georges Salameh, David Chalet and Michael Deligant

Machines 2026, 14(9), 1067; https://doi.org/10.3390/machines14091067 (registering DOI) - 17 Sep 2026

Abstract

To reduce greenhouse gas emissions, fuel cell powertrains represent a promising alternative for heavy-duty transport. Such demanding applications require an extended operational lifespan, which calls for models able to accurately map internal states as a function of system architecture and control strategy. This [...] Read more.

To reduce greenhouse gas emissions, fuel cell powertrains represent a promising alternative for heavy-duty transport. Such demanding applications require an extended operational lifespan, which calls for models able to accurately map internal states as a function of system architecture and control strategy. This work presents a pseudo-2D macro-homogeneous proton exchange membrane fuel cell model, discretized along the flow channels, in which the various transport phenomena are resolved between layers but not within their thickness. This choice reflects the model’s purpose: integration into complete system models to support system architecture studies, which requires a suitable trade-off between computation time and representativeness of system-imposed operating conditions. Kulikovsky’s analytical approximation is used to compute the voltage losses in the catalyst layer, preserving an accuracy close to a model with a fully discretized catalyst layer thickness. The model is integrated into a system featuring anode recirculation and no cathode humidification to study the sensitivity of humidity distribution to operating parameters. Simulations show that the anode recirculation rate and the temperature difference between inlet and outlet are the two main operating conditions governing spatial humidity distribution, while coolant inlet temperature, pressure, and cathode stoichiometry predominantly affect the absolute humidity within the stack. Full article

(This article belongs to the Topic Mobility Engineering and Sustainability)

17 pages, 3014 KB

Open AccessArticle

Cutting Force Estimation from Feed Drive Current via Inverse Filtering

by F. Reichel, G. N. Sahu, A. Otto and S. Ihlenfeldt

Machines 2026, 14(9), 1066; https://doi.org/10.3390/machines14091066 (registering DOI) - 17 Sep 2026

Abstract

This paper presents a virtual sensor for the in-process prediction of cutting forces from feed drive current measurements in milling processes via inverse filtering. Components of the feed drive current in ball-screw drives that are related to inertia, friction, and gravity are separated [...] Read more.

This paper presents a virtual sensor for the in-process prediction of cutting forces from feed drive current measurements in milling processes via inverse filtering. Components of the feed drive current in ball-screw drives that are related to inertia, friction, and gravity are separated from the cutting-force-related component via models or air-cutting experiments. Impact hammer tests are then used to identify the transfer function between forces at the tool tip and the corresponding response at the feed drive. The proposed inverse filtering approach completes the virtual sensor for online monitoring of cutting forces based on feed drive current signals. Compared to existing approaches, which are mainly based on Kalman-filter or deep learning models, this method avoids the additional effort for modeling, parameter identification and generation of training data. Experimental results are presented for cutting tests on a three-axis turn-milling center. The prediction error between the virtual sensor and the measured cutting forces lies between 6% and 17%, depending on the cutting parameters. In general, the virtual sensor can be implemented in any feed drive system with a minimal effort for parameter identification. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Tool Condition Monitoring)

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50 pages, 21681 KB

Open AccessArticle

3-UPU-1-S Parallel Mechanism for Biomechanical Emulation of Human Ankle Motion in a Transtibial Prosthesis: Mechanical Design, Kinematic Evaluation, and Control Implementation

by John Alexander Baca Rodriguez, Christian Estanislao Barrientos Quispe, Mahdi Tavakoli and Deyby Huamanchahua

Machines 2026, 14(9), 1065; https://doi.org/10.3390/machines14091065 (registering DOI) - 17 Sep 2026

Abstract

The human ankle exhibits complex multiplanar behavior involving plantarflexion, dorsiflexion, inversion, eversion, and coupled orientation changes that are essential for balance and terrain adaptation. This work presents the design, kinematic evaluation, mechanical implementation, and control validation of a transtibial prosthesis based on a [...] Read more.

The human ankle exhibits complex multiplanar behavior involving plantarflexion, dorsiflexion, inversion, eversion, and coupled orientation changes that are essential for balance and terrain adaptation. This work presents the design, kinematic evaluation, mechanical implementation, and control validation of a transtibial prosthesis based on a parallel robotic mechanism. Three candidate architectures, 3-SPS-1-S, 3-UPU-1-S, and 3-UCU-1-S, were compared through inverse and forward kinematics, orientational workspace, Jacobian conditioning, constructability, and mechatronic-integration criteria. The corresponding workspace coverages were 74.1%, 75.7%, and 72.2%, respectively. The 3-UPU-1-S architecture exhibited a median Jacobian condition number of 12.63, a 95th-percentile value of 20.56, no numerically singular configurations within the evaluated feasible workspace, and the highest VDI-2225 technical score (0.913), supporting its final selection. The selected mechanism was manufactured and integrated into a functional laboratory prototype with distributed ESP32-S3-based electronics, position sensing, inertial measurement, and closed-loop actuation. Experimental periodic tests showed that the decentralized PID controller achieved dominant-axis RMSE values of 3.53° in pitch during dorsiflexion–plantarflexion and 4.61° in roll during inversion–eversion. A revised formal LQRI controller was evaluated separately using the identified actuator-space model. Its nominal closed-loop system was asymptotically stable, with

\max Re (λ) = - 1.6896

, and robustness simulations showed mean-RMSE reductions of approximately 9.4–16.6% relative to PID across the evaluated perturbation scenarios. These results demonstrate the feasibility of the proposed 3-UPU-1-S mechanism as an integrated laboratory platform for multiplanar transtibial-prosthesis research while identifying the need for future dynamic load-bearing and user-centered validation. Full article

(This article belongs to the Special Issue Mechanical Design of Parallel Manipulators)

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27 pages, 5014 KB

Open AccessArticle

Dynamic Modelling and Parameter Optimisation of Friction-Induced Panhead Pitching and Dual-Strip Load Redistribution in a Metro Pantograph–Rigid Overhead Contact Line System

by Jiayu Fu, Jinfa Guan and Junqing Chen

Machines 2026, 14(9), 1064; https://doi.org/10.3390/machines14091064 (registering DOI) - 17 Sep 2026

Abstract

Unequal load sharing between collector strips causes local contact deterioration in metro rigid overhead contact line (ROCL) systems, whereas conventional equivalent-strip models cannot resolve independent strip contact or friction-induced panhead pitching. This study develops a coupled pantograph–ROCL model with independent vertical degrees of [...] Read more.

Unequal load sharing between collector strips causes local contact deterioration in metro rigid overhead contact line (ROCL) systems, whereas conventional equivalent-strip models cannot resolve independent strip contact or friction-induced panhead pitching. This study develops a coupled pantograph–ROCL model with independent vertical degrees of freedom for the two strips and a panhead pitch degree of freedom. The ROCL is represented by planar Euler–Bernoulli beam elements with Hermite interpolation and coupled to the pantograph through two moving unilateral contacts. The baseline model is validated against measured contact-force statistics from a Guangzhou Metro line. A representative low-temperature, low-humidity, snow-free scenario is then introduced through test-informed friction variation, modified support stiffness and small support-height deviations. The scenario has little effect on mean contact force but increases force fluctuation, impact peaks, panhead pitching and local poor-contact risk. Sensitivity-guided derivative-free optimisation of local panhead parameters reduces the maximum pitch angle, total-contact-force standard deviation and dual-strip load-imbalance index by 38.24%, 23.09% and 44.43%, respectively. The model provides a mechanical framework for evaluating friction-induced pitch vibration and improving dual-strip load sharing in rigid current-collection systems. Full article

(This article belongs to the Section Friction and Tribology)

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27 pages, 2172 KB

Open AccessArticle

LEADI: Operating-Mode-Aware Machine Condition Monitoring for Leak-Related Energy Anomalies—A Before-and-After Maintenance Study of a Single Production Asset

by Tanya Titova and Rosen Kosturkov

Machines 2026, 14(9), 1063; https://doi.org/10.3390/machines14091063 - 17 Sep 2026

Abstract

Compressed-air leaks create persistent parasitic demand, but machine-level condition monitoring is difficult because air consumption changes strongly with operating mode. LEADI (Leak Energy Anomaly Detection Index) was developed as an operating-mode-aware procedure that evaluates the deviation of directly measured flow rate from a [...] Read more.

Compressed-air leaks create persistent parasitic demand, but machine-level condition monitoring is difficult because air consumption changes strongly with operating mode. LEADI (Leak Energy Anomaly Detection Index) was developed as an operating-mode-aware procedure that evaluates the deviation of directly measured flow rate from a local reference baseline derived from a stable post-repair condition with maintained pressure and low within-window variability. The method was developed on days 1–5 and evaluated on held-out days 6–7 from two one-week campaigns conducted before and after implementation of the prescribed corrective actions. With 60 min windows, LEADI flagged 19/19 evaluable pre-repair and 0/17 post-repair windows, with diagnostic coverage of 39.6% and 35.4%, respectively. A simple fifth-percentile flow comparator without operating-mode selection flagged 47/48 versus 1/48 windows. This shows that the low-flow region itself contains strong discriminatory information for separating the two periods. The role of the operating-mode layer is to restrict engineering interpretation to pre-specified eligible operating conditions. The flow-rate difference within the diagnostic operating condition was 122.0 L/min (95% CI 114.6–132.3). Over a common 168 h basis, measured volume decreased by 1370.8 m³ (28.90%), while a separate check normalized by pressurized time gave 28.12%. Because the specific energy consumption of the compressor station was not measured, the energy effect is reported only as a scenario for the same 168 h. The results support the applicability of LEADI as a selective decision-support layer for the investigated asset and the two observed conditions, without establishing universal leak detection or causal attribution of the observed change to individual defects. Full article

(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)

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29 pages, 13405 KB

Open AccessArticle

An Adaptive Fault Features Localization Method for Wind Turbine Bearing via Graph Signal Spectrum Enhancement

by Peng Xu, Yiding Liu, Huaming Zhang, Yousheng Yang, Dian Liu, Yonggang Xu and Lei Feng

Machines 2026, 14(9), 1062; https://doi.org/10.3390/machines14091062 - 17 Sep 2026

Abstract

Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited by fixed graph topology, empirical feature selection and poor noise robustness. This [...] Read more.

Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited by fixed graph topology, empirical feature selection and poor noise robustness. This paper proposes an adaptive frequency graph spectrum (AFGS) model for bearing fault diagnosis. The model constructs graph signals in the frequency domain and determines the core analysis interval adaptively via eigenvalue sequences, which eliminates fixed topology constraints. Combined with a fast bisection search framework and a correlation spectral negative entropy (CSNE) index sensitive to periodic fault impulses, the proposed method realizes fully automatic optimal band selection without manual intervention and improves noise resistance. The AFGS method first transforms vibration signals via fast Fourier transform and constructs frequency-domain graph features based on Laplacian matrix decomposition. The optimal fault characteristic band is adaptively determined using the bisection framework and CSNE criterion. Finally, signal reconstruction and envelope spectrum analysis are implemented for fault identification. Simulation results under −3 dB low signal-to-noise ratio show that AFGS can effectively extract the 1st to 8th fault harmonics. Further validation on the measured inner and outer race fault signals of 6205 bearings demonstrates that the proposed method can clearly identify fault characteristic frequencies and their multi-order harmonics. Comparative tests with Fast Kurtogram and Autogram indicate that the two benchmark algorithms only extract limited low-order harmonics under simulated noise and completely fail in practical strong noise environments. Experimental results verify that AFGS outperforms conventional methods in band localization accuracy, noise suppression, and fault feature extraction completeness, providing a reliable solution for the bearing fault diagnosis of rotating machinery under complex working conditions. Full article

(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)

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25 pages, 28268 KB

Open AccessArticle

A Physics-Based Framework for Predicting Assembly-Induced Superharmonic Responses in Spline-Coupled Rotor–Casing Systems

by Xiaole Guan, Xin Jin, Zhijing Zhang, Zhilong Luo and Chan Wang

Machines 2026, 14(9), 1061; https://doi.org/10.3390/machines14091061 - 17 Sep 2026

Abstract

Casing assembly deviations can disrupt aero-engine support alignment and induce abnormal vibration. However, the mechanism by which bearing-seat coaxiality errors generate superharmonic responses remains insufficiently understood. This study investigates how such deviations propagate through support misalignment and a spline coupling to influence the [...] Read more.

Casing assembly deviations can disrupt aero-engine support alignment and induce abnormal vibration. However, the mechanism by which bearing-seat coaxiality errors generate superharmonic responses remains insufficiently understood. This study investigates how such deviations propagate through support misalignment and a spline coupling to influence the vibration response of a coupled rotor–casing system. A geometric relationship is formulated to transform the misalignment of the support into equivalent parallel and angular initial offsets at the spline coupling. An additional excitation model for the spline coupling, accounting for meshing stiffness, transmitted torque, tooth-side clearance, unilateral tooth contact, and relative whirl motion, is incorporated into a reduced-order whole-engine dynamic model. The integrated model is evaluated under a range of experimentally measured coaxiality conditions. Numerical simulations predict critical response regions near 16,000 and 23,000 r/min, along with subcritical resonance peaks at approximately 7800 and 12,500 r/min that are attributed to superharmonic excitation mechanisms rather than conventional mass unbalance alone. Experimental results indicate that the 0.234 mm coaxiality condition yields larger vibration amplitudes, additional low-speed resonance peaks, and more pronounced 2X–4X harmonic components compared with the 0.069 mm condition, thereby corroborating the proposed assembly deviation mechanism under cold-state structural dynamic conditions. Full article

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23 pages, 2305 KB

Open AccessArticle

Semi-Supervised Gearbox Anomaly Detection Under Variable Operating Conditions

by Yubo Shao, Huibo Chang, Lingyun Yang and Wei Li

Machines 2026, 14(9), 1060; https://doi.org/10.3390/machines14091060 - 17 Sep 2026

Abstract

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends [...] Read more.

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends for the selected time- and frequency-domain statistics, and the deviations from these trends form the statistical residuals. Computed order tracking then converts the vibration signal to the angular domain, where five mechanism features describe meshing energy, harmonic structure, sideband modulation, and order-spectrum entropy. Removing the corresponding healthy speed trends yields the mechanism residuals. Robust Bounded Health-Consistency Weighting (RB-HCW) weights these residuals according to their variability in healthy data before they are fused with the statistical residuals and modeled by Deep Support Vector Data Description (DeepSVDD). The Sequential Bayesian Queue-Based Alarm (SBQA) module then confirms whether abnormal decisions persist across successive windows. Across the four fault types under the two separately modeled load conditions, the proposed method achieved macro-averaged true positive rate (TPR), accuracy (ACC), and F1-score values of 93.31%, 92.58%, and 94.02%, respectively. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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18 pages, 6711 KB

Open AccessArticle

Dynamic Behavior Modeling of Solenoid Valves Used for Proportional Fuel Control: PWM-Based Flow Rate Prediction

by Aydın Hacı Dönmez, Yaşar Mutlu and Pegah Mutlu

Machines 2026, 14(9), 1059; https://doi.org/10.3390/machines14091059 - 17 Sep 2026

Abstract

This study presents a predictive modeling framework for estimating transient flow rates in PWM-driven solenoid valves. Building on a previously validated dynamic model, the proposed framework enables flow prediction under varying process conditions and valve configurations. Flow rates at fully open conditions are [...] Read more.

This study presents a predictive modeling framework for estimating transient flow rates in PWM-driven solenoid valves. Building on a previously validated dynamic model, the proposed framework enables flow prediction under varying process conditions and valve configurations. Flow rates at fully open conditions are obtained using computational fluid dynamics (CFD) and validated experimentally. CFD analyses are further performed at partial valve openings, and the resulting data are incorporated into a numerical algorithm based on piecewise linear interpolation for prediction throughout the opening–closing cycle of the valve. The model is validated under PWM operation, and parametric analyses are conducted to examine the effects of duty ratio, period, and coil voltage. The maximum difference between the experimental and numerical results was 1.3% under fully open conditions and 2.8% under PWM operation, demonstrating good agreement across the investigated conditions. By incorporating flow characteristics at intermediate spool positions that cannot be directly measured experimentally, the proposed approach allows accurate prediction of both transient and time-averaged flow rates. This provides a computationally efficient alternative to fully coupled transient CFD simulations. Full article

(This article belongs to the Special Issue Control and Mechanical System Engineering, 2nd Edition)

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28 pages, 2046 KB

Open AccessArticle

Time-Series Machine Learning for Fault Diagnosis and Severity Estimation in Industrial Processes

by Paraskevi Zacharia, Styliani Kontaki, Konstantinos Moustris and Constantinos Stergiou

Machines 2026, 14(9), 1058; https://doi.org/10.3390/machines14091058 - 17 Sep 2026

Abstract

Industrial fault detection and diagnosis are essential for maintaining operational reliability and minimizing performance degradation in process industries. This study presents an integrated machine learning framework for fault detection, fault-type classification, and fault severity estimation using a synthetic chemical-process time-series dataset comprising six [...] Read more.

Industrial fault detection and diagnosis are essential for maintaining operational reliability and minimizing performance degradation in process industries. This study presents an integrated machine learning framework for fault detection, fault-type classification, and fault severity estimation using a synthetic chemical-process time-series dataset comprising six reactors operating under multiple conditions. The framework combines a five-class fault diagnosis model with a gated severity estimation stage that is activated only when a fault is detected, enabling simultaneous assessment of process condition and operational impact. Eight process variables were selected through statistical and process-oriented analysis, while one-minute difference features and reactor identity information were incorporated to capture short-term process dynamics and equipment-specific operating characteristics. The framework was evaluated using an episode-aware methodology incorporating fault-episode partitioning, leakage-prevention measures, grouped cross-validation, and episode-level analysis. The selected classification model achieved a balanced accuracy of 76.25% and a macro F1-score of 82.44%. For severity estimation, the complete end-to-end pipeline achieved R² = 0.153 across active-fault observations, illustrating the impact of fault detection errors on downstream severity assessment. When evaluated across all observations, including predominantly normal conditions, the corresponding R² increased to 0.871. Under an oracle scenario using the true fault type, severity estimation achieved R² = 0.920. The results provide fault-specific and episode-level insights and support a proof of concept within this synthetic industrial process environment. Full article

(This article belongs to the Section Industrial Systems)

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58 pages, 51921 KB

Open AccessArticle

Automatic Inspection of Flexible Parts Using Virtual Fixturing

by Pierre Boulanger

Machines 2026, 14(9), 1057; https://doi.org/10.3390/machines14091057 - 16 Sep 2026

Abstract

A flexible part has no unique shape until it is constrained, which makes dimensional inspection difficult. Standard practice clamps it in a dedicated jig and probes it with a coordinate measuring machine or a range sensor. We replace the jig with virtual fixturing. [...] Read more.

A flexible part has no unique shape until it is constrained, which makes dimensional inspection difficult. Standard practice clamps it in a dedicated jig and probes it with a coordinate measuring machine or a range sensor. We replace the jig with virtual fixturing. From partial range views of the unfixtured part, the pipeline recovers a coarse pose between the scan and the CAD model using a robust geodesic bilateral curvature algorithm, deforms the model towards the scan by non-rigid registration, and computes deviations along the model surface normal to decide whether the part is in tolerance. On a prismatic part and a game-controller housing, the method is more accurate than optimal-step non-rigid ICP, radial basis function FEM, and coherent point drift, because the generated deformations come from an operator closely related to the proposed method’s own regularizer, those margins favor it by construction and are reported with that caveat. On a generated thin-shell test part the measurement uncertainty of the implementation is measured at about 0.039 mm, dominated by the registration rather than by the sensor. The pipeline is then exercised on a physically scanned injection-molded engine cover, a 612 mm part whose free-state residual against its nominal model is 4.62 mm at the verified global optimum. No independent coordinate-measuring-machine reference was available for that part, so this experiment is reported as a free-state residual and a controlled comparison with and without the feature set, not as a statement of absolute measurement accuracy. Full article

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

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23 pages, 10054 KB

Open AccessArticle

Rapid Failure Analysis of Train Derailment Potential Under Mixed Loading and Track Conditions

by Reza Naseri, Brennan L. Gedney and Dimitrios C. Rizos

Machines 2026, 14(9), 1056; https://doi.org/10.3390/machines14091056 - 16 Sep 2026

Abstract

Train derailments pose a critical failure mode in railway systems, often resulting in severe safety hazards and significant financial losses. Understanding how train loading patterns interact with track deficiencies is essential for effective failure analysis and prevention. This paper introduces the Rapid Vehicle–Track [...] Read more.

Train derailments pose a critical failure mode in railway systems, often resulting in severe safety hazards and significant financial losses. Understanding how train loading patterns interact with track deficiencies is essential for effective failure analysis and prevention. This paper introduces the Rapid Vehicle–Track Interaction (R-VTI) as a framework to simulate the complexities of dynamic train–track interactions. Central to the framework is the novel Pseudo-Dynamic Coupling (PDC) technique, which enables computation of wheel–rail dynamic forces with substantially greater computational efficiency than currently used coupling techniques. The R-VTI framework supports a wide range of solver techniques and subsystem coupling schemes, making it adaptable for different simulation requirements. The framework is validated against Federal Railroad Administration field measurements, achieving agreement within 5% error. A case study of different train–track configurations shows that the framework can quickly detect when loading patterns and track conditions exceed derailment thresholds. Axle-level results reveal that unloaded cars near the front or middle of the train increase the likelihood of derailment-failure modes. The efficiency of the R-VTI framework enables large-scale scenario analysis, supporting both optimized loading strategies and targeted track maintenance. By providing a robust and scalable solution, the R-VTI framework advances derailment potential assessment practices, offering a practical tool for improving railway safety and operational resilience. Full article

(This article belongs to the Special Issue From Data to Insights: Applying Advanced Analytics to Railroad Maintenance Diagnostics and Prognostics)

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24 pages, 2482 KB

Open AccessArticle

A Chirp-Rate-Driven Adaptive Window Chirplet Transform and Its Application in Bearing Fault Diagnosis

by Zhonghao Liu, Gang Yu and Tian Ran Lin

Machines 2026, 14(9), 1055; https://doi.org/10.3390/machines14091055 - 16 Sep 2026

Abstract

In this paper, we propose an instantaneous chirp-rate-driven adaptive window Chirplet transform algorithm for the analysis of strong time-varying nonlinear frequency-modulated signals with uncorrelated components. In this approach, the length of the sliding window in the Chirplet transform is dynamically adjusted according to [...] Read more.

In this paper, we propose an instantaneous chirp-rate-driven adaptive window Chirplet transform algorithm for the analysis of strong time-varying nonlinear frequency-modulated signals with uncorrelated components. In this approach, the length of the sliding window in the Chirplet transform is dynamically adjusted according to the estimated instantaneous chirp rate of each signal component of an initial time–frequency result from short-time Fourier transform (STFT). A boundary constraint determined from the modal support intervals of the signal is utilized to restrain the allowable searching frequency range of the instantaneous frequency (IF) trajectories and incorporated into a cost-function-based IF extraction method to improve accuracy in the IF estimation. The effectiveness of the proposed algorithm is validated using a simulated nonlinear frequency-modulated (FM) signal with two uncorrelated components, and two sets of experimental bearing vibration signals. It is shown that the proposed algorithm can accurately track the frequency modulation of a strong FM signal dynamically to render an accurate estimation of the IFs and modal amplitudes of a strong FM signal. A comparison study also verifies that the proposed algorithm can produce a better energy-concentrated time–frequency result compared to other commonly employed time–frequency analysis techniques, particularly when the signal is contaminated by noise. Full article

(This article belongs to the Special Issue Artificial Intelligence in Wind Energy Optimization Design)

58 pages, 12260 KB

Open AccessReview

Digital Intelligence-Enabled Green Scheduling in Dynamic Job Shops: A Review

by Adilanmu Sitahong, Ruili Zhao, Yiping Yuan, Xinpeng Nie and Peiyin Mo

Machines 2026, 14(9), 1054; https://doi.org/10.3390/machines14091054 - 16 Sep 2026

Abstract

Dynamic job shops must absorb new orders, machine failures and processing time variations while operating under increasingly demanding energy and carbon constraints. In such settings, an offline schedule may become obsolete soon after release, especially when production and energy states evolve on different [...] Read more.

Dynamic job shops must absorb new orders, machine failures and processing time variations while operating under increasingly demanding energy and carbon constraints. In such settings, an offline schedule may become obsolete soon after release, especially when production and energy states evolve on different time scales. Digital twins, data-driven models and artificial intelligence methods now make it possible to sense shop floor changes, anticipate their effects and revise schedules through feedback. This review organises the emerging literature through a ‘four loops and one layer’ framework: perception, modelling and prediction, intelligent decision-making, and execution feedback form the operating cycle, while continuous learning spans successive scheduling rounds. Studies are examined along three distinct but related dimensions—dynamic events, green objectives and digital intelligence methods. Within this D-G-I framework, the literature reveals a move from static optimisation to adaptive scheduling, from efficiency-centred formulations to coordinated efficiency–energy–carbon objectives, and from stand-alone rules towards combinations of data, models and domain knowledge. Yet the evidence remains uneven. Data–model coupling is often weak, transfer across production settings is limited, and genuinely closed-loop industrial validation is rare. These limitations make explainable decision-making, cross-scenario adaptation and digital twin-enabled closed-loop optimisation central priorities for subsequent research. Full article

(This article belongs to the Section Industrial Systems)

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26 pages, 1524 KB

Open AccessArticle

A Hybrid Data–Physics Residual Network with Class-Orthogonal Physics Heads for EMAT Lamb-Wave Fault Diagnosis on Rail-Steel Plates

by Shao-Xuan Zhang, Hai-Dong Song and Yi-Yao Zhang

Machines 2026, 14(9), 1053; https://doi.org/10.3390/machines14091053 - 16 Sep 2026

Abstract

Steel rails are critical components of industrial dynamic transportation systems, and their in-service fault diagnosis demands reliable discrimination of multiple defect categories under multi-mode Lamb-wave dispersion and sample-level physical-parameter drift. The rail surface is modelled by a thin metal plate instrumented with two [...] Read more.

Steel rails are critical components of industrial dynamic transportation systems, and their in-service fault diagnosis demands reliable discrimination of multiple defect categories under multi-mode Lamb-wave dispersion and sample-level physical-parameter drift. The rail surface is modelled by a thin metal plate instrumented with two EMAT probes (the standard laboratory surrogate for in-service rail inspection), and the proposed architecture is evaluated on this rail-equivalent plate geometry. Purely data-driven one-dimensional classifiers plateau near 80% test accuracy on a 25,000-sample simulated EMAT A-scan benchmark, while conventional physics-informed neural networks (PINNs) that inject the physical prior only at the loss-function level fail to break this ceiling. We propose a hybrid data–physics residual network, the proposed EMAT-PINN, that couples a convolutional backbone with a logit-orthogonal four-head architecture tying each defect class—hole, crack, corrosion, weld—to one simulator-derived physical quantity (reflected energy, S0/A0 ratio, arrival time, or dispersion shift) via a bias-free additive projection of the class logit. The bias-free construction guarantees that deleting or zeroing any head collapses the affected class logit to the shared baseline, so the remaining heads cannot reroute around the missing head—a structural non-replaceability that supports explainable fault diagnosis. Combined with a four-term physics regression loss (

λ_{phys} = 2.0

), the resulting proposed EMAT-PINN attains 95.05% test accuracy at only 0.195 M parameters, with every knockout ablation dropping the model below the

80 %

threshold commonly referenced as a practical acceptance benchmark. This per-class mapping provides an auditable link between the model’s internal representation and the physical scattering mechanism behind each decision, directly supporting explainable fault diagnosis in industrial deployment. Full article

(This article belongs to the Special Issue Data-Driven and AI-Based Fault Diagnosis for Industrial Dynamic Systems)

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36 pages, 7554 KB

Open AccessArticle

Design Analysis of Line Start Synchronous Motor with Salient Poles for Efficiency Improvement

by Vasilija Sarac, Dragan Minovski, Sara Aneva and Darko Bogatinov

Machines 2026, 14(9), 1052; https://doi.org/10.3390/machines14091052 - 16 Sep 2026

Abstract

Line-start synchronous motors (LSSMs) have emerged as a promising alternative to induction motors in response to increasingly stringent energy-efficiency requirements. However, the salient-pole permanent-magnet rotor topology has been comparatively less studied than other LSSM rotor configurations, particularly with respect to the influence of [...] Read more.

Line-start synchronous motors (LSSMs) have emerged as a promising alternative to induction motors in response to increasingly stringent energy-efficiency requirements. However, the salient-pole permanent-magnet rotor topology has been comparatively less studied than other LSSM rotor configurations, particularly with respect to the influence of its design parameters on motor efficiency and dynamic performance. Initially, the effects of the number of rotor bars and the dimensions of the squirrel-cage winding on motor performance are investigated, and the optimal configuration with respect to starting torque and synchronization capability is selected. The chosen design is then subjected to efficiency optimization by varying four design parameters. An optimetric analysis is employed to evaluate numerous parameter combinations under predefined operating conditions, generating a wide range of motor models. The configuration achieving the highest efficiency is subsequently identified and selected. In addition, the optimization with same design parameters is carried out using Genetic Algorithms (GA), confirming the results obtained through the optimetric analysis. The transient responses of speed, torque, and current are subsequently investigated for both the initial and optimized motor model. The analysis provides insight into the effects of the optimization process on motor starting performance, synchronization capability and steady-state operation enabling appropriate conclusions to be drawn. Full article

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41 pages, 60411 KB

Open AccessReview

A Review of the Development and Research Status of Multi-Blade Centrifugal Fans

by Dongmei Wang, Henghui Liao, Ye Chu, Guo Tang and Hao Chang

Machines 2026, 14(9), 1051; https://doi.org/10.3390/machines14091051 - 16 Sep 2026

Abstract

With the growing global emphasis on environmental protection, energy conservation, and emission reduction, along with rapid advances in precision machinery and manufacturing technologies, energy-efficient multi-blade centrifugal fans have become a key research area worldwide. These devices, commonly referred to as multi-blade centrifugal fans, [...] Read more.

With the growing global emphasis on environmental protection, energy conservation, and emission reduction, along with rapid advances in precision machinery and manufacturing technologies, energy-efficient multi-blade centrifugal fans have become a key research area worldwide. These devices, commonly referred to as multi-blade centrifugal fans, utilize an impeller with multiple blades to transport air or gas, operating on the same fundamental principles as conventional centrifugal fans. Due to their ability to deliver high flow rates with high efficiency and stable performance, they are widely used in ventilation, air conditioning, and industrial applications. This paper provides a comprehensive review of recent advances in the research and development of multi-blade centrifugal fans. It covers key aspects of component design and optimization, including the impeller, volute, collector, and rim clearance. The review also addresses critical issues, including rotating stall, vibration, noise generation, and material selection. Furthermore, it highlights emerging perspectives, including the application of entropy production theory to elucidate flow mechanisms, strategies for performance optimization, advanced technologies for material innovation, and protective devices to enhance operational reliability and functionality. This review provides guidance for future research and development aimed at improving the efficiency and performance of multi-blade centrifugal fans across diverse applications. Full article

(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)

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21 pages, 13290 KB

Open AccessArticle

Nonsmooth Gear-Contact Vibration Suppression Under Variable-Speed Operation Using Phase-Consistent Modelling and Bounded Line-of-Action Force Learning

by Mingzhen Zhang and Anwen Shen

Machines 2026, 14(9), 1050; https://doi.org/10.3390/machines14091050 - 16 Sep 2026

Abstract

Nonsmooth gear-contact vibration under variable-speed operation is strongly affected by mesh-phase evolution, backlash-induced contact switching, and unilateral tooth contact. This study investigates local line-of-action vibration suppression in a single-stage spur-gear pair performing a repetitive acceleration–braking–cruising task. The modelling contribution is a control-oriented hybrid-coordinate [...] Read more.

Nonsmooth gear-contact vibration under variable-speed operation is strongly affected by mesh-phase evolution, backlash-induced contact switching, and unilateral tooth contact. This study investigates local line-of-action vibration suppression in a single-stage spur-gear pair performing a repetitive acceleration–braking–cruising task. The modelling contribution is a control-oriented hybrid-coordinate formulation that separates the prescribed mean-speed motion from local dynamic transmission error (DTE), reconstructs the time-varying mesh stiffness (TVMS) phase from the mean angle and local relative displacement, and retains a distinct mean-coordinate phase for transmission error (TE). The learning contribution is a bounded line-of-action force strategy that combines finite-time application, low-pass filtering, plateau DC removal, amplitude and rate constraints, and trial-level contact-response acceptance and rollback. Thus, an input-feasible candidate can still be rejected when its executed contact response violates the prescribed limits. Under the nominal same-initial-condition baseline, the trade-off iteration reduces DTE and mesh-force AC RMS by 11.52% and 24.85%, respectively. Contact loss decreases from 3.35% to 0.55%, and re-engagements decrease from 29 to 5. In 100 paired perturbation samples, 94 cases improve both RMS measures. The results support the proposed framework within the tested numerical task and perturbation range. Full article

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

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