Search Results (643)

Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses. Full article

This paper addresses fault diagnosis in nonlinear systems where reliable mathematical models are unavailable and only input–output measurements are accessible. The proposed methodology consists of three stages. First, a data-driven identification stage is performed using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to capture the nonlinear dynamics of the system from fault-free sensor data. This procedure yields a set of convex Takagi–Sugeno (TS) models representing the system dynamics. In the second stage, fault detection is achieved using zonotopic proportional–integral (PI) observers with convex structures. Robustness against parametric uncertainty and sensor noise is ensured through an ${\mathcal{H}}_{\infty }$ formulation expressed as a set of linear matrix inequalities (LMIs). Finally, fault isolation is carried out using a fault signature matrix (FSM). The zonotopic framework provides adaptive set-based residual bounds that act as adaptive thresholds for fault detection, while structured residual activation patterns enable reliable fault isolation. The proposed approach is evaluated on a continuous stirred tank reactor (CSTR) under sensor faults and incipient process faults in the presence of measurement noise and compared with representative data-driven methods. Results demonstrate improved diagnostic accuracy and reduced false-alarm rates while maintaining timely fault detection and reliable isolation. Full article

This study aimed to analyze the relationships between the electrical properties of fresh seed melon pulp and storage conditions, thereby providing a rapid electrical method for quality evaluation. The electrical parameters of fresh seed melon were measured using the parallel-plate electrode method at different storage temperatures of 4 °C, 8 °C, 12 °C, 16 °C, and 20 °C, and storage times of 0, 2, 4, 6, and 8 h. The relationships between electrical parameters and quality attributes at different frequencies were further investigated. An adaptive neuro-fuzzy inference system (ANFIS) model was established to predict the quality characteristics of fresh seed melon, with electrical parameters used as input variables and quality characteristics used as output variables. Eight membership function models were constructed and compared to select the optimal prediction model. The results showed that, with increasing frequency, the impedance (Z), capacitance (Cp), and resistance (Rp) of fresh seed melon decreased, while conductance (G) and reactance (X) increased at different storage temperatures. At different storage times, Z, quality factor (Q), Cp, and Rp decreased, while G and X increased with increasing frequency. The variation ranges of Z, Cp, Rp, G, and X gradually decreased at higher frequencies. Significant correlations between electrical parameters and quality characteristics were observed at the characteristic test frequency of 163.28 kHz. The ANFIS results showed that gauss2mf was the optimal model for predicting cohesiveness (Co, R² = 0.9491), pimf was the optimal model for predicting chewiness (Ch, R² = 0.9595), and gbellmf was the optimal model for predicting resilience (Re, R² = 0.9596). These results indicate that the combination of electrical properties and the ANFIS model has potential for evaluating the quality characteristics of fresh seed melon under the present experimental conditions. This study provides a theoretical basis for quality detection and storage preservation of fresh seed melon and offers a reference for the development of rapid electrical quality-evaluation techniques for seed melon pulp. Full article

Pseudocereals such as quinoa, amaranth, and buckwheat have attracted increasing attention as ingredients for extruded snack products because of their nutritional value, gluten-free status, and content of bioactive compounds. The quality of extruded products is governed by complex interactions among processing variables, including barrel temperature, screw speed, feed moisture content, and formulation characteristics. As a result, mathematical modelling has become an important tool for predicting product properties and identifying suitable processing conditions. This review summarizes modelling approaches applied to extruded food products with a focus on pseudocereal extrusion. Particular emphasis is placed on response surface methodology (RSM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and hybrid optimisation strategies. Published studies indicate that RSM remains the most commonly used approach because of its simplicity and interpretability, while ANN-based models generally provide much higher predictive accuracy when strong nonlinear relationships are present. The widespread use of small experimental datasets and limited external validation remains a major challenge for the practical implementation of advanced machine-learning models. This review examines the strengths and limitations of current modelling approaches and discusses future opportunities for integrating predictive models with digital manufacturing frameworks. Full article

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

The integration of renewable energy into smart grids is beneficial for a sustainable future and the environment. Still, it has challenges such as energy optimisation, environmental conditions and power quality degradation. Existing Maximum Power Point Tracking (MPPT) techniques often focus on tracking efficiency under steady-state conditions, ignoring the impact of real-time variation in environmental conditions and load. The predictive power flow control (PPFC) algorithm is available with one or more fixed MPPT algorithms. No studies have reported on how the choice of MPPT affects PPFC harmonic mitigation. This paper addresses both concerns through a systematic comparative analysis of MPPT techniques integrated with a PPFC method to mitigate harmonics in renewable-integrated smart grid systems. To address this research gap, a comprehensive comparative analysis of various MPPT techniques, such as Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and hybrid Machine Learning (ML) techniques, integrated with PPFC to achieve effective harmonic mitigation in a smart grid environment is conducted. A 3 MW solar farm integrated with a battery storage system is modelled in MTALB/Simulink 2025b under real-time varying conditions, such as environmental and load variations over time in Auckland, New Zealand. The study focuses on key performance parameters such as total harmonic distortion (THD), power loss, stability and efficiency. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller, integrated with forecast-based power flow control, achieved overall performance by providing higher efficiency (97.5%), effective harmonic mitigation, and enhanced system stability under the nonlinear behaviour of the photovoltaic system. The proposed ANFIS-based system ensured a stable and smooth power output under varying environmental conditions, outperforming conventional and other intelligent MPPT techniques. Full article

Series resistance (Rs) is a key parameter that limits photovoltaic cell performance; however, its conventional estimation from current–voltage measurements requires electrical contact and controlled testing conditions. In this study, a nondestructive image-based methodology is proposed to estimate Rs in crystalline silicon photovoltaic cells using electroluminescence images and a Sugeno-type adaptive neuro-fuzzy inference system. A dataset of 666 electroluminescence images was processed using normalized grayscale histogram descriptors, and reference Rs values were obtained from I-V characterization. Three global radiometric descriptors corresponding to low-, medium-, and high-intensity pixel fractions were used as model inputs. The ANFIS model achieved high predictive performance, with a testing RMSE of 0.0065 Ω and an R² value of >0.98. These results indicate that the global EL intensity distributions contain information related to resistive losses. However, further validation under different acquisition conditions and using independent datasets is required before field-scale deployment. The proposed approach provides a compact and interpretable framework for rapid photovoltaic cell screening based on electroluminescence imaging, complementing conventional electrical characterization under controlled laboratory conditions. Full article

We present a neuro-fuzzy digital twin for cardiac disease recognition on the PTB-XL dataset that keeps the accuracy of a strong convolutional model while exposing its reasoning as readable fuzzy rules. The key design choice is to separate the two jobs instead of forcing one network to do both. A convolutional backbone (xresnet1d101 with cross-lead attention) is trained first and acts as the predictor. A second phase then freezes the backbone and trains a five-expert mixture of interval Type-2 Adaptive Neuro-Fuzzy Inference Systems as an interpretation layer, attached through a learnable trust gate. Four experts read complementary signal streams (the raw 12-lead waveform, a Fourier–Bessel Series Expansion, a Tunable-Q Wavelet Transform, and a morphogram); a fifth reads 22 clinical and demographic descriptors. A concept bottleneck maps the backbone embedding to eight named clinical concepts (ST elevation, T inversion, QT prolongation, Sokolov–Lyon and Cornell voltages, RVH, axis deviation, QRS widening), so the downstream rules read in clinical language. Two genetic-algorithm stages keep the model compact: NSGA-II selects the clinical features, and a multi-objective rule-pruning search trades macro-F1 and hypertrophy recall against the number of active rules. On the official test fold the twin reaches a macro-F1 of 0.751 (bootstrap 95% CI 0.740 to 0.767), a macro-AUC of 0.928, a Matthews correlation coefficient of 0.672, and a macro expected calibration error of 0.021, which is above the Strodthoff xresnet1d101 baseline of 0.74 and well below the calibration error of our earlier coupled design. Because the fuzzy layer is attached as a trust-gated residual and the best epoch is chosen on threshold-optimized validation F1, the interpretable twin matches the backbone within about 0.004 (twin 0.751 versus backbone 0.754) rather than paying the usual “interpretability tax”. The fuzzy mixture on its own still reaches a macro-F1 of 0.69, so the rules carry real diagnostic signal. Subgroup macro-F1 stays within about 0.045 across sex, age, and body-mass groups. A concept-level intervention simulator estimates how cardioactive drug classes would shift a patient’s risk by perturbing the named concepts and re-running the same twin. Full article

In precision machining, tool wear is one of the primary factors affecting machining quality and production efficiency. This study developed intelligent prediction models for tool wear in the high-speed milling (HSM) of AISI 1045 medium-carbon steel by integrating robust process design with backpropagation neural networks (BPNN) and adaptive neuro-fuzzy inference systems (ANFIS). Robust process design was employed to optimize the machining parameters, while the hyperparameters of both BPNN and ANFIS models were systematically optimized to improve prediction performance. Tool wear was measured after a fixed cutting length and used to establish the prediction models. The optimized machining parameters reduced tool wear by 53% compared with the worst experimental condition. The optimized BPNN model achieved a prediction accuracy of 96.68%, whereas the ANFIS model with Gaussian membership functions achieved 100%, demonstrating superior predictive performance. The proposed approach effectively combines robust process design and intelligent prediction models to accurately predict tool wear using a limited experimental dataset, providing an efficient methodology for tool wear prediction and machining parameter optimization in intelligent manufacturing applications. Full article

Global navigation satellite system (GNSS) has the characteristics of high-precision positioning, which makes it an essential part of mobile terminal positioning. In urban environments, satellite signals are easily blocked and reflected, which affects the positioning results. In this case, inertial sensors manufactured by Micro Electromechanical Systems (MEMS) technology become the key to achieving continuous positioning. Although the integration of a micro inertial system (MINS) and GNSS provides the continuous output of position information, the position error will increase with time. This paper proposes a prediction model based on an adaptive neuro-fuzzy inference system (ANFIS) and a method to obtain model parameters. The model takes the position error $\delta P^b$ of the carrier system as the output, and the rejection time (the time when GNSS positioning information remains unavailable), accelerometer data, and gyroscope data as the model inputs. Aiming at the model parameters, the consequent parameters acquisition method based on the least squares method and the antecedent parameters acquisition method based on a genetic algorithm are proposed. When GNSS outages last for 60 s, the maximum value of horizontal positioning error is reduced by 80% in six outage sections. Therefore, the proposed method in this paper is a potential method to predict the positioning error of a MINS/GNSS integrated navigation system. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup table methods. Although such approaches are simple, reliable, and easy to implement, they lack the ability to adaptively adjust the braking force allocation according to varying driving conditions, thereby limiting the potential for high efficiency energy recovery. To improve regenerative energy recovery while simultaneously maintaining braking stability, this study introduces an ANFIS-optimized Sliding Mode–Fuzzy Joint Hierarchical Control Strategy (S-FJHCS) for regenerative braking systems. In the upper control layer, an improved tire road friction coefficient estimation algorithm is integrated with a sliding mode controller to ensure consistent slip ratio regulation between the front and rear wheels. In the lower control layer, a fuzzy control algorithm is employed to coordinate the distribution of braking torque between the hydraulic braking system and the hub motors. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized to perform offline optimization of the fuzzy controller, enabling the adaptive adjustment of fuzzy rules and membership functions based on historical operating conditions. Simulation and experimental results demonstrate that the proposed regenerative braking control strategy can improve regenerative energy recovery efficiency by approximately 5–10% compared with a conventional rule based regenerative braking strategy, while maintaining satisfactory braking performance and vehicle stability under various driving conditions. Full article

Decision-making in cyber defense exercises (CDX) is shaped by technical, emotional, motivational, and collaborative human factors under uncertainty and time pressure. This study proposes a human-centered Adaptive Neuro-Fuzzy Inference System (ANFIS) framework to model and predict Counterfactual Decision Reflection (CDR) outcomes in CDX environments. Two complementary datasets representing technical, emotional, motivational, and teamwork-related dimensions were collected from the international Lithuanian Armed Forces cyber defense exercise Amber Mist 2024 and analyzed using Spearman correlation, 3D regression surface modeling, fuzzy rule extraction, and ANFIS prediction to investigate the relationship between human factors and CDR. The results demonstrated that teamwork, communication, and collaboration have a stronger influence on decision stability than isolated technical competencies. Baseline ANFIS evaluation indicated that triangular membership functions provided the best generalization, while generalized bell functions achieved the lowest training errors. To improve model robustness, multiple synthetic data augmentation methods were evaluated. The augmented ANFIS models substantially improved predictive performance, reducing testing error values significantly. The findings confirm that synthetic-data-enhanced neuro-fuzzy modeling provides an effective and interpretable framework for analyzing human-centered cybersecurity decision-making processes in cyber defense exercises. Full article

The accumulation of waste tyres presents a significant environmental challenge owing to their non-biodegradable nature and limited recycling options. The incorporation of tyre-derived rubber into concrete offers a promising strategy to reduce landfill waste and lower the consumption of natural aggregates. This study presents an integrated experimental and machine learning-based framework for evaluating and predicting the performance of rubberized concrete. M25-grade concrete mixtures were prepared with partial replacement of coarse aggregates by waste tyre rubber at proportions of 0%, 10%, 20%, and 30% by volume. Mechanical performance was assessed through compressive and split-tensile strength tests, whereas durability was evaluated using water absorption measurements. Microstructural characterization was conducted using scanning electron microscopy and X-ray diffraction analysis. In parallel, predictive models based on artificial neural networks, adaptive neuro-fuzzy inference systems, and fuzzy logic were developed and validated using statistical measures. The results showed that increasing rubber content reduced mechanical strength and increased water absorption due to weaker interfacial bonding and higher porosity. Nevertheless, concrete containing a 10% rubber replacement retained approximately 90% of the control strength while maintaining satisfactory durability. The machine learning models demonstrated strong predictive accuracy for estimating concrete properties. Overall, the findings suggest that limited incorporation of waste tyre rubber can contribute to the development of sustainable and low-carbon concrete materials with reduced embodied energy and environmental impact. Full article

This study presents the design and development of a wearable lower-limb exoskeleton system aimed at supporting motion assistance in monoplegia-related conditions. The proposed approach integrates a simplified sensing configuration with a data-driven neuro-fuzzy control framework based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). Motion data are acquired from the healthy limb using bend flex sensors and are used to generate control signals for the actuation of the impaired limb through an Arduino-based embedded platform. The mechanical structure is developed using a lightweight 3D-printed design combined with high-torque DC motors and gear transmission mechanisms. Experimental evaluation conducted under controlled conditions demonstrates that the system is capable of capturing and reproducing fundamental motion patterns, with the ANFIS model providing a consistent mapping between sensor inputs and actuator responses. The obtained results indicate a satisfactory level of performance for motion pattern reproduction, particularly in terms of temporal behavior and transition between movement states. The presented system emphasizes low-cost implementation, computational efficiency, and practical implementation, making it suitable as a proof-of-concept framework for wearable assistive technologies. While the results demonstrate the feasibility of the proposed approach for motion reproduction, further studies involving extended testing and user-specific adaptation are required to assess its potential applicability in real-world scenarios. Full article