Search Results (838)

Electric motors play a decisive role in electric vehicles by converting electrical energy into mechanical motion across various drivetrain components. However, failures in these motors can interrupt the motor function, with approximately 40% of these failures stemming from bearing issues. Key contributors to bearing degradation include shaft voltage, bearing current, and electric discharge material spalling current, especially in motors powered by inverters or variable frequency drives. This review explores the tribological and vibrational aspects of bearing currents, analyzing their mechanisms and influence on electric motor performance. It addresses the challenges faced by electric vehicles, such as high-speed operation, elevated temperatures, electrical conductivity, and energy efficiency. This study investigates the origins of bearing currents, damage linked to shaft voltage and electric discharge material spalling current, and the effects of lubricant properties on bearing functionality. Moreover, it covers various methods for measuring shaft voltage and bearing current, as well as strategies to alleviate the adverse impacts of bearing currents. This comprehensive analysis aims to shed light on the detrimental effects of bearing currents on the performance and lifespan of electric motors in electric vehicles, emphasizing the importance of tribological considerations for reliable operation and durability. The aim of this study is to address the engineering problem of bearing failure in inverter-fed EV motors by integrating electrical, tribological, and lubrication perspectives. The novelty lies in proposing a conceptual link between lubricant breakdown and damage morphology to guide mitigation strategies. The study tasks include literature review, analysis of bearing current mechanisms and diagnostics, and identification of technological trends. The findings provide insights into lubricant properties and diagnostic approaches that can support industrial solutions. Full article

This study presents the fabrication and characterization of sustainable polylactic acid (PLA)-based biocomposites reinforced with bio-origin fillers derived from food waste: seashells, eggshells, walnut shells, and spent coffee grounds. All fillers were introduced at 15 wt% into a commercial PLA matrix modified with a compatibilizer to improve interfacial adhesion. Mechanical properties (tensile, flexural, and impact strength), morphological characteristics (via SEM), and hydrolytic aging behavior were evaluated. Among the tested systems, PLA reinforced with seashells (PLA15S) and coffee grounds (PLA15C) demonstrated the most balanced mechanical performance, with PLA15S achieving a tensile strength increase of 72% compared to neat PLA. Notably, PLA15C exhibited the highest stability after 28 days of hydrothermal aging, retaining ~36% of its initial tensile strength, outperforming other systems. In contrast, walnut-shell-filled composites showed the most severe degradation, losing over 98% of their mechanical strength after aging. The results indicate that both the physicochemical nature and morphology of the biofiller play critical roles in determining mechanical reinforcement and degradation resistance. This research underlines the feasibility of valorizing agri-food residues into biodegradable, semi-structural PLA composites for potential use in sustainable packaging or non-load-bearing structural applications. Full article

The main bearing of a tunnel boring machine (TBM) is a critical component of the main driving system that enables continuous excavation, and its performance is crucial for ensuring the safe operation of the TBM. Currently, there are few testing technologies for TBM main bearings, and a comprehensive testing and evaluation system has yet to be established. This study presents an experimental investigation using a self-developed, full-scale TBM main bearing test bench. Based on a representative load spectrum, both operational condition tests and life cycle tests are conducted alternately, during which the signals of the main bearing are collected. The observed vibration signals are weak, with significant vibration attenuation occurring in the large structural components. Compared with the test bearing, which reaches a vibration amplitude of 10 g in scale tests, the difference is several orders of magnitude smaller. To effectively utilize the selected evaluation indicators, the entropy weight method is employed to assign weights to the indicators, and a comprehensive analysis is conducted using grey relational analysis. This strategy results in the development of a comprehensive evaluation method based on entropy weighting and grey relational analysis. The main bearing performance is evaluated under various working conditions and the same working conditions in different time periods. The results show that the greater the bearing load, the lower the comprehensive evaluation coefficient of bearing performance. A multistage evaluation method is adopted to evaluate the performance and condition of the main bearing across multiple working scenarios. With the increase of the test duration, the bearing performance exhibits gradual degradation, aligning with the expected outcomes. The findings demonstrate that the proposed performance evaluation method can effectively and accurately evaluate the performance of TBM main bearings, providing theoretical and technical support for the safe operation of TBMs. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

Geogrids are frequently utilized in engineering for reinforcement; yet, they are vulnerable to construction damage when employed on coarse-grained soil subgrades. In contrast, waste tire grids are more appropriate for subgrade reinforcement owing to their rough surfaces, integrated steel meshes, robust transverse ribs, extended degradation cycles, and superior durability. Based on the limit equilibrium theory, this study developed formulae for calculating the internal and external frictional resistance, as well as the end resistance of waste tires, to ascertain the interface bearing properties and calculation techniques of waste tire grids. Based on this, a mechanical model for the ultimate pull-out resistance of waste-tire-reinforced soil was developed, and its validity was confirmed through a series of pull-out tests on single-sided strips, double-sided strips, and tire grids. The results indicated that the tensile strength of one side of the strip was approximately 43% of that of both sides, and the rough outer surface of the tire significantly enhanced the tensile performance of the strip; under identical normal stress, the tensile strength of the single-sided tire grid was roughly nine times and four times greater than that of the single-sided and double-sided strips, respectively, and the grid structure exhibited superior anti-deformation capabilities compared to the strip structure. The average discrepancy between the calculated values of the established model and the theoretical values was merely 2.38% (maximum error < 5%). Overall, this research offers technical assistance for ensuring the safety of subgrade design and promoting environmental sustainability in engineering, enabling the effective utilization of waste tire grids in sustainable reinforcement applications. Full article

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

The Sierra Minera de Cartagena-La Unión, located in southeast of the Iberian Peninsula, has been significantly impacted by historical mining activities, which resulted in environmental degradation, including acid mine drainage (AMD) and heavy metal contamination. This study evaluates the potential of PRISMA hyperspectral imagery for multi-temporal mapping of AMD-related minerals in two mining-affected drainage basins: Beal and Gorguel. Key minerals indicative of AMD—iron oxides and hydroxides (hematite, jarosite, goethite), gypsum, and aluminium-bearing clays—were identified and mapped using band ratios applied to PRISMA data acquired over five dates between 2020 and 2024. Additionally, Sentinel-2 data were incorporated in the analysis due to their higher temporal resolution to complement iron oxide and hydroxide evolution from PRISMA. Results reveal distinct temporal and spatial patterns in mineral distribution, influenced by seasonal precipitation and climatic factors. Jarosite was predominant after torrential precipitation events, reflecting recent AMD deposition, while gypsum exhibited seasonal variability linked to evaporation cycles. Goethite and hematite increased in drier conditions, indicating transitions in oxidation states. Validation using X-ray diffraction (XRD), laboratory spectral curves, and a larger time-series of Sentinel-2 imagery demonstrated strong correlations, confirming PRISMA’s effectiveness for iron oxides and hydroxides and gypsum identification and monitoring. However, challenges such as noise, striping effects, and limited image availability affected the accuracy of aluminium-bearing clay mapping and limited long-term trend analysis. Full article

Patients undergo total knee arthroplasty (TKA) at younger ages with the expectation that the devices will perform well over two to three decades. During this time, the ultra-high molecular weight polyethylene (UHMWPE) bearing material properties of the implant may change due to aging induced by radiation and oxygen diffusion or other effects. Vitamin E or other antioxidants are promoted since several years to improve the oxidation resistance of UHMWPE. To compare the effectivity of these substances against established materials, a six weeks aging process was used and the chemical, mechanical and bio-tribological properties were analysed. Highly crosslinked and two weeks aged UHMWPE served as a reference for the currently established aging standards and virgin UHMWPE was aged for six weeks to separate the effects of crosslinking and vitamin E blending. Six weeks artificially aging changed the chemical, mechanical and bio-tribological properties of cross-linked UHMWPE significantly compared to only two weeks artificially aging, leading to cracks and delamination during the highly demanding activities wear test. The degradative effect of extended aging was also observed for virgin UHMWPE. These observations are in good accordance to retrieval findings. Minor changes on the chemical properties were observed for the cross-linked UHWMPE blended with vitamin E without impact on the mechanical and bio-tribological properties. Full article

In recent years, domain adaptation (DA) has been extensively applied to predicting the remaining useful life (RUL) of bearings across conditions. Although traditional DA-based methods have achieved accurate predictions, most methods fail to extract multi-scale degradation information, focus only on global-scale DA, and ignore the importance of temporal weights. These limitations hinder further improvements in prediction accuracy. This paper proposes a novel model, called the health indicator-weighted subdomain alignment network (HIWSAN), which first learns feature representations at multiple scales, then constructs health indicators as temporal weights, and finally performs subdomain-level alignment. Two case studies based on the XJTU-SY and PRONOSTIA datasets were conducted, covering ablation, comparison, and generalization experiments to evaluate the proposed HIWSAN. Experimental results show that HIWSAN achieves an average MAE of 0.0989 and an average RMSE of 0.1189 across two datasets, representing reductions of 21.07% and 25.13%, respectively, compared to existing state-of-the-art methods. Full article

Background/Objectives: An increasing number of apparently healthy individuals are adhering to a gluten-free lifestyle without any underlying medical indications, although the evidence for the health benefits in these individuals remains unclear. Although it has already been shown that a low- or gluten-free diet alters the gut microbiota, few studies have examined the effects of this diet on healthy subjects. Therefore, our aim was to evaluate whether and how a prolonged low-gluten diet impacts gut microbiota composition and function in healthy adults, bearing in mind its intimate link to the host’s health. Methods: Forty healthy volunteers habitually consuming a gluten-containing diet (HGD, high-gluten diet) were included in a randomised control trial consisting of two successive 8-week dietary intervention periods on a low-gluten diet (LGD). After each 8-week period, gut microbiota composition was assessed by 16S rRNA gene sequencing, molecular quantification by qPCR, and a cultural approach, while its metabolic capacity was evaluated through measuring faecal fermentative metabolites by ¹H NMR. Results: A prolonged period of LGD for 16 weeks reduced gut microbiota richness and decreased the relative abundance of bacterial species with previously reported potential health benefits such as Akkermansia muciniphila and Bifidobacterium sp. A decrease in certain plant cell wall polysaccharide-degrading species was also observed. While there was no major modification affecting the main short-chain fatty acid profiles, the concentration of the intermediate metabolite, ethanol, was increased in faecal samples. Conclusions: A 16-week LGD significantly altered both composition and metabolic production of the gut microbiota in healthy individuals, towards a more dysbiotic profile previously linked to adverse effects on the host’s health. Therefore, the evaluation of longer-term LDG would consolidate these results and enable a more in-depth examination of its impact on the host’s physiology, immunity, and metabolism. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

Cylindrical roller bearings are widely used in industrial machinery, automotive systems, and aerospace applications, where their reliability directly affects the performance and safety of mechanical systems. The fatigue life of cylindrical roller bearings is significantly affected by their elastohydrodynamic lubrication condition, with variations potentially reaching multiple times. However, conventional quasi-static models often neglect lubrication effects. This study establishes a quasi-static analysis model for cylindrical roller bearings that incorporates the effects of elastohydrodynamic lubrication by integrating elastohydrodynamic lubrication theory with the Lundberg–Palmgren life model. The isothermal line contact elastohydrodynamic lubrication equations are solved using the multigrid method, and the contact load distribution is determined through nonlinear iterative techniques to calculate bearing fatigue life. Taking the N324 support bearing on the main shaft of an SFW250-8/850 horizontal hydro-generator as an example, the influences of radial load, inner race speed, and lubricant viscosity on fatigue life are comparatively analyzed. Experimental validation is conducted under both light-load and heavy-load operating conditions. The results demonstrate that elastohydrodynamic lubrication markedly increases contact loads, leading to a reduced predicted fatigue life compared with that of the De Mul model (which ignores lubrication). The proposed lubrication-integrated model achieves an average deviation of 5.3% from the experimental data, representing a 16.1% improvement in prediction accuracy over the De Mul model. Additionally, increased rotational speed and lubricant viscosity accelerate fatigue life degradation. Full article

The predictive performance of the remaining useful life (RUL) estimation model for bearings is of utmost importance, and the setting method of the bearing degradation threshold is crucial for detecting its early degradation point, as it significantly affects the performance of the RUL prediction model. To solve these problems, a bearing RUL prediction method based on early degradation detection and optimized BiLSTM is proposed: an optimized VMD combined with the Pearson correlation coefficient is used to denoise the bearing signal. Afterward, multi-domain features are extracted and evaluated using different metrics. The optimal degradation feature is then selected. Furthermore, KPCA is used to integrate the features and establish the health indicators (HIs) for early degradation detection of bearings using a sliding window method combined with the 3σ (3-sigma) criterion and the quartile method. The RUL prediction model is developed by combining the BiLSTM network with the attention mechanism and by employing the SSA to adaptively update the network parameters. The proposed RUL prediction model is tested on various datasets to evaluate its generalization ability and applicability. The obtained results demonstrate that the proposed denoising method has high performance. The dynamic 3σ-threshold setting method accurately detects the early degradation points of bearings. The proposed RUL prediction model has high performance and fitting capacity, as well as very high generalization ability and applicability, enabling the early prediction of bearing RUL. Full article

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed to identify rolling bearing degradation states under different working conditions. The experimental study collects the vibration signals of nine degradation states, including the different sizes of inner and outer ring local faults as well as normal conditions, to comparatively analyze the proposed method’s rapid calibration capability and feature extraction quality. Furthermore, t-SNE visualization is utilized to analyze the network response to bearing degradation features. Finally, the degradation state identification performance across different network architectures is compared in pattern recognition experiments. The results show that the proposed improved feature extraction method significantly reduces the iterative calibration computational burden while effectively extracting local fault degradation information and overcoming complex working condition influence. The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. The overall recognition accuracy can reach 97.33%. Full article