Search Results (490)

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article

This study investigates the deformation and fracture mechanisms of a Ti₆₁Al₁₆Cr₁₀Nb₈V₅ multi-principal element alloy (Ti61V5 alloy) under quasi-static and dynamic compression. The alloy comprises an equiaxed BCC matrix (~35 μm) with uniformly dispersed nano-sized B2 precipitates and a ~3.5% HCP phase along grain boundaries, exhibiting a density of 4.82 g/cm³, an ultimate tensile strength of 1260 MPa, 12.8% elongation, and a specific strength of 262 MPa·cm³/g. The Ti61V5 alloy exhibits a pronounced strain-rate-strengthening effect, with a strain rate sensitivity coefficient (m) of ~0.0088 at 0.001–10/s. Deformation activates abundant {011} and {112} slip bands in the BCC matrix, whose interactions generate jogs, dislocation dipoles, and loops, evolving into high-density forest dislocations and promoting screw-dominated mixed dislocations. The B2 phase strengthens the alloy via dislocation shearing, forming dislocation arrays, while the HCP phase enhances strength through a dislocation bypass mechanism. At higher strain rates (960–5020/s), m increases to ~0.0985. Besides {011} and {112}, the BCC matrix activates high-index slip planes {123}. Intensified slip band interactions generate dense jogs and forest dislocations, while planar dislocations combined with edge dislocation climb enable obstacle bypassing, increasing the fraction of edge-dominated mixed dislocations. The Ti61V5 alloy shows low sensitivity to adiabatic shear localization. Under forced shear, plastic-flow shear bands form first, followed by recrystallized shear bands formed through a rotational dynamic recrystallization mechanism. Microcracks initiate throughout the shear bands; during inward propagation, they may terminate upon encountering matrix microvoids or deflect and continue when linking with internal microcracks. Full article

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments. Full article

(1) Background: Understanding how forest management practices regulate hydrological cycles is critical for sustainable water resource management and addressing global water crises. However, the effects of light-felling (selective thinning) on hydrological processes in temperate mixed forests remain poorly understood. This study comprehensively evaluated the impacts of light-felling intensity levels on three hydrological layers (canopy, litter, and soil) in mid-rotation Korean pine (Pinus koraiensis) forests managed under the “planting conifer and preserving broadleaved trees” (PCPBT) system on Changbai Mountain, China. (2) Methods: Hydrological processes—including canopy interception, throughfall, stemflow, litter interception, soil water absorption, runoff, and evapotranspiration—were measured across five light-felling intensity levels (control, low, medium, heavy, and clear-cutting) during the growing season. The stand structure and precipitation characteristics were analyzed to elucidate the driving mechanisms. (3) Results: (1) Low and heavy light-felling significantly increased the canopy interception by 18.9%~57.0% (p < 0.05), while medium-intensity light-felling reduced it by 20.6%. The throughfall was significantly decreased 10.7% at low intensity but increased 5.3% at medium intensity. The stemflow rates declined by 15.8%~42.7% across all treatments. (2) The litter interception was reduced by 22.1% under heavy-intensity light-felling (p < 0.05). (3) The soil runoff rates decreased by 56.3%, 16.1%, and 6.5% under the low, heavy, and clear-cutting intensity levels, respectively, although increased by 27.1% under medium-intensity activity (p < 0.05). (4) The monthly hydrological dynamics shifted from bimodal (control) to unimodal patterns under most treatments. (5) The canopy processes were primarily driven by precipitation, while litter interception was influenced by throughfall and tree diversity. The soil processes correlated strongly with throughfall. (4) Conclusions: Low and heavy light-felling led to enhanced canopy interception and reduced soil runoff and mitigated flood risks, whereas medium-intensity light-felling supports water supply during droughts by increasing the throughfall and runoff. These findings provide critical insights for balancing carbon sequestration and hydrological regulation in forest management. Full article

Pellets can be produced not only from forest dendromass but also from agricultural dendromass derived from short rotation coppice (SRC) plantations, as well as surplus straw from cereal and oilseed crops. This study aimed to determine the thermophysical properties and elemental composition of 16 types of pellets produced from four types of forest biomass (Scots pine I, alder, beech, and Scots pine II), four types of agricultural biomass (SRC willow, SRC poplar, wheat straw, and rapeseed straw), and eight types of pellets from mixtures of wood biomass and straw. Another aim of the study was to demonstrate which pellet types met the parameters specified in three standards, categorizing pellets into thirteen different classes. As expected, pellets produced from pure Scots pine sawdust exhibited the best quality. The quality of the pellets obtained from mixtures of dendromass and straw deteriorated with an increase in the proportion of cereal straw or rapeseed straw in relation to pure Scots pine sawdust and SRC dendromass. The bulk density of the pellets ranged from 607.9 to 797.5 kg m⁻³, indicating that all 16 pellet types met the requirements of all six classes of the ISO standard. However, it was determined that four types of pellets (rapeseed, wheat, and two others from biomass mixtures) did not meet the necessary requirements of the Premium and Grade 1 classes. The ash content ranged from 0.44% DM in pellets from pure Scots pine sawdust to 5.00% DM in rapeseed straw pellets. Regarding ash content, only the pellets made from pure Scots pine sawdust met the stringent requirements of the highest classes, A1, Premium, and Grade 1. In contrast, all 16 types of pellets fulfilled the criteria for the lower classes, i.e., Utility and Grade 4. Concerning the nitrogen (N) content, seven types of pellets met the strict standards of classes A1 and Grade 1, while all the pellets satisfied the less rigorous requirements of classes B and Grade 4. Full article

From 2016 to 2021, a field experiment was conducted in the North China Plain to study the long-term effects of drip irrigation and nitrogen coupling on the growth, biomass allocation, and irrigation water and fertilizer use efficiency of short-rotation triploid Populus tomentosa plantations. The experiment adopted a completely randomized block design, with one control (CK) and six water–nitrogen coupling treatments (IF, two irrigation levels × three nitrogen application levels). Data analysis was conducted using ANOVA, regression models, Spearman’s correlation analysis, and path analysis. The results showed that the effects of water and nitrogen treatments on the annual increment of diameter at breast height (ΔDBH), annual increment of tree height (ΔH), basal area of the stand (BA_S), stand volume (V_S), and annual forest productivity (AFP) in short-rotation forestry exhibited a significant stand age effect. The coupling of water and nitrogen significantly promoted the DBH growth of 2-year-old trees (p < 0.05), but after 3 years of age, the promoting effect of water and nitrogen coupling gradually diminished. In the 6th year, the above-ground biomass of Populus tomentosa was 5.16 to 6.62 times the under-ground biomass under different treatments. Compared to the I45 treatment (irrigation at soil water potential of −45 kPa), the irrigation water use efficiency of the I20 treatment (−20 kPa) decreased by 88.79%. PFP showed a downward trend with the increase in fertilization amount, dropping by 130.95% and 132.86% under the I20 and I45 irrigation levels. Path analysis indicated that irrigation had a significant effect on the BA_S, V_S, AFP, and TGB of 6-year-old Populus tomentosa (p < 0.05), with the universality of irrigation being higher than that of fertilization. It is recommended to implement phased water and fertilizer management for Populus tomentosa plantations in the North China Plain. During 1–3 years of tree age, adequate irrigation should be ensured and nitrogen fertilizer application increased. Between the ages of 4 and 6, irrigation and fertilization should be ceased to reduce resource wastage. This work provides scientific guidance for water and fertilizer management in short-rotation plantations. Full article

Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models often overlook. The current study presents a machine learning (ML) framework for the prediction and understanding of hydraulic permeability in fibrous biomaterials with a micropolar fluid. A dataset of 1000 numerical simulations was generated by varying the micropolar fluid properties and the fiber volume fraction in a periodic porous structure with nine parallel cylindrical fibers in a square lattice. Six powerful ML algorithms were deployed: Decision Trees (DT), Random Forests (RF), XGBoost, LightGBM, Support Vector Regression (SVR), and k-Nearest Neighbors (kNN). The balance of predictive capacity to unseen data values (tracking $R^2$ values and error metrics) with computational efficiency for all algorithms was assessed. The best-performing ML algorithm was subsequently used to interpret the decisions made by the model using Shapley Additive exPlanations (SHAP) analysis and understand the role of feature importances. The SHAP findings highlight the potential of ML in capturing complex fluid interactions and guiding the design of advanced fibrous biomaterials with optimized hydraulic permeability. Full article

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories. Full article

Gear condition monitoring is predominantly executed through the utilization of acceleration sensors positioned on the housing. However, recent advancements have identified measuring the instantaneous angular speed as a compelling alternative as it shortens the transmission path and therefore provides high-quality rotational angle information that can be used to increase damage prediction accuracy, particularly under transient operating conditions. Additionally, there are a variety of methodologies for integrating sensors into gears, which underscores the necessity for high-quality condition data. However, it should be noted that a significant amount of effort is required to successfully integrate these sensors into the rotating system. This publication uses a gear wheel sensor that employs the gear itself as a material measure to acquire rotational angle data and to deduce the damage condition. A magnetoresistive sensor is integrated into the gearbox housing radially facing a ferromagnetic gear and measures the rotational angle by the gear teeth. Various artificial tooth flank damages are applied to the pinion. The rotational angle is measured with the gear sensor, and the damage state is classified with a random forest classifier using established evaluations in the time and frequency domains. The tests are conducted under stationary operating conditions at an array of speed and torque levels. Additionally, they are performed under transient operating conditions, employing speed ramps at constant torque. The results of the classification are evaluated by means of classification accuracy and confusion matrices and compared with those obtained via a classic encoder at the pinion shaft and an acceleration sensor at the gearbox housing. Full article

The Brazilian forestry sector comprises 9.94 million hectares of plantations, with eucalyptus dominating 75% of this area for pulp production. Technological advances have enhanced machinery performance, with the cut-to-length system being the primary method for pulpwood production. This study aimed to optimize the operational cycle of the log loader by evaluating productivity, operational cycles, and loading costs. Data were collected in Bahia, northeastern Brazil, from a forestry company operating under varying productivity scenarios and forest rotations. Time and motion studies were conducted to assess the log loader’s cycles, while productivity and cost analyses were performed. The results indicated that predictive models effectively explained productivity variations. The log loader’s productivity increased with the average volume per tree (AVT) and decreased with the number of movements, which consumed 68% of the cycle time due to wood adjustment and stack organization. Stages such as personal breaks, truck movements, crane adjustments, and cleaning of fallen material showed no significant statistical differences. Loading costs rose by up to 154% with increased movements and decreased with a higher AVT. Additionally, loading tri-train trucks significantly influenced transportation efficiency, emphasizing the importance of optimizing the log loader’s cycle to balance costs and enhance transportation operations. Full article

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement. Full article

Rotation and organic material addition (e.g., biochar) are major measures to improve soil quality, but the improvement effects and mechanisms of their combination on soil quality remain unclear; the relationship between the physical, chemical, and biological parameters was has not been adequately detected in terms of the change in quality after biochar addition. This study selected corn straw biochar as the material and established two biochar application methods: biochar mixed in 0–20 cm soil depth (B1) and biochar mixed in 0–40 cm soil depth (B2). After 3 years of maize–bean rotation, soil samples from 0–20 cm and 20–40 cm were collected to determine the soil’s physical, chemical, and biological properties, as well as crop yields. Principal component analysis was used to establish a minimum data set for the systematic analysis of soil quality and its factors. The results showed that compared with the control (CK), biochar reduced soil bulk density by 3.1% and electrical conductivity by 19.5–28.25% while increasing soil organic matter content by 7.2%, ammonium nitrogen content by 6.7–12.0%, available nitrogen content by 6.7–18.5%, available phosphorus content by 15.6–23.8%, available potassium content by 11.6–17.3%, soil urease activity by 12.25–21.6%, soil sucrase activity by 6.8–30.8%, soil neutral phosphatase activity by 5.6–9.7%, and soil catalase activity by 13.6%. Four indicators, namely bulk density, water content, pH, and nitrate nitrogen, were selected from 16 soil-quality-related indicators to form the minimum data set (MDS), and the soil quality index was calculated. Biochar application significantly increased the soil quality index (SQI) of rotation soil by 14.6–63.3% and crop yields by 5.6–7.2%. A random forest analysis of soil indicators and crop yields, combined with partial least squares structural equation modeling, revealed that biological indicators—particularly catalase activity—showed significant positive correlations with crop yields. Based on these multi-dimensional analyses, the interaction between rotation systems and biochar application improves the quality of mollisol soil plow layers by reducing bulk density and increasing catalase activity. Full article

The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming–pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a multi-sensor remote sensing framework for monitoring crop distribution and analyzing rotation dynamics. After cloud removal and Savitzky–Golay filtering were applied to correct noise, we selected vegetation index features with maximum inter-class separability during the optimal classification window (June 15–August 20) and generated quarterly Sentinel-1 SAR composites. A Random Forest classifier was employed to perform crop classification based on these optimized features, enabling 10 m resolution crop mapping from 2019 to 2023. The proposed method achieved high classification accuracy (overall accuracy and Kappa > 0.90), with strong agreement between mapped and statistical crop areas (R²: 0.85–0.88; RMSE: 0.42–0.58 × 10⁴ ha). Spatial analysis revealed distinct distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in northern Zhangjiakou, while maize dominated southern regions. We observed significant annual variations in crop area proportions and identified specific altitudinal preferences: maize, potato, and sesame were mainly grown at 480–520 m, while oats and other crops at 520–600 m. Slope analysis showed that most crops were cultivated on gentle slopes of 0–5°, with sesame extending to 4–10° slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation was primarily monoculture. Key drivers of rotation change included water scarcity, economic incentives, and continuous cropping constraints. These findings provide critical insights for optimizing crop rotation strategies, enhancing agricultural sustainability, and improving land-use efficiency in ecologically fragile regions. Full article