Search Results (1,321)

Can machine learning generate statistically validated alpha in equity markets while adapting to changing market conditions? This study addresses this question by proposing a regime-aware LightGBM framework conditioned on market regimes detected via a rolling Hidden Markov Model, eliminating look-ahead bias. Backtested on 51 NASDAQ-100 constituents (2015–2026), the strategy achieved a portfolio Sharpe ratio of 1.18 (95% CI: [0.53, 1.84]) and outperformed four baseline models. The key findings include the following: (i) cross-asset features (Bitcoin as a leading indicator) contribute the most predictive value; (ii) macroeconomic indicators outweigh traditional technical indicators for high-beta stocks; (iii) the model autonomously adapts its decision logic across regimes, shifting from mean reversion in bear markets to risk appetite monitoring in bull markets. While block bootstrap tests confirm statistical significance ($p<0.001$), the Deflated Sharpe Ratio (0.69) does not reach formal significance after multiple testing correction—an honest finding we report transparently. Full article

To solve the copper brittleness problem of copper-bearing steel, support the ferritic rolling process, and ensure the continuity of rolling across different phase regions, this study focused on copper-bearing steel with w(Cu) = 1.56%. Gleeble thermal simulation tests were conducted to investigate the deformation behavior of Cu-bearing steel, and a corresponding deformation resistance model was established; meanwhile, the precipitation characteristics of the second phase were characterized by high-resolution transmission electron microscopy (HRTEM). The results show that the deformation resistance of copper-bearing steel increases with decreasing temperature and increasing strain rate, and its deformation resistance–temperature curve shows a unique bimodal trend, where the inflection point at 840 °C is attributed to the austenite–ferrite phase transformation, and the inflection point at 920 °C is caused by the precipitation of B2-FeCu ordered nanoclusters. HRTEM observations confirm that these nanoclusters are metastable phases with a size of less than 5 nm, and their orientation relationship with the matrix is (011)_B2//(011)_α-Fe and [001]_B2//[001]_α-Fe. The area fraction of B2-FeCu ordered nano-precipitates is in the range of 4.27% to 5.32%, which can reduce the lattice distortion of the matrix and thus decrease dislocation slip resistance. The segmented modified Zhou-Guan model has a coefficient of determination (R²) greater than 0.96 between the predicted and experimental values, which can accurately guide the optimization of low-temperature rolling process parameters for copper-bearing steel. Full article

Slewing bearing is a rotating component with high load-carrying capacity, which is an important part of the crane connecting the upper rotating parts and the lower supporting parts; therefore, it is of great significance to analyze the performance of slewing bearings. This paper establishes a theoretical model and an integrated finite element model for the mechanical performance of slewing bearings, and the results of the two show high consistency. The influences of four bearing parameters (contact angle, raceway curvature radius coefficient, rolling element diameter, and number of rolling elements) and three bolt parameters (number of bolts, bolt preload, and washer thickness) on the mechanical performance of the slewing bearing were studied, aiming to provide a reference basis for the selection and design of crane slewing bearings. Full article

Rolling bearing faults are the primary cause of rotating machinery failure. Under complex operating conditions, the weak fault impact signals are easily overwhelmed by strong noise and exhibit significant non-stationary characteristics, posing severe challenges to accurate diagnosis. To address this, this paper proposes an improved Transformer-based fault diagnosis method driven by the improved black-winged kite algorithm-variational mode decomposition (IBKA-VMD) and hierarchical fractional-order attention entropy (HFrAttE). The method employs the integrated multi-strategy IBKA to adaptively determine the optimal parameters of VMD, utilizes HFrAttE to construct highly discriminative feature sets, and further builds an improved Transformer model integrating bidirectional attention mechanisms and feature decoupling structures for deep feature mining. The classification decision is finalized by the twin extreme learning machine (TELM). Experimental results on the case western reserve university (CWRU) bearing dataset under different noise environments (−2 dB, −5 dB) demonstrate that the proposed method maintains 100% accuracy, recall, and F1-score under −5 dB noise interference, significantly outperforming comparative models. It exhibits excellent anti-noise performance and feature extraction capability, providing an efficient solution for intelligent operation and maintenance of rotating machinery under complex operating conditions. Full article

This paper presents a Proof of Concept (PoC) for PredictiveMaintenance MCP, an open-source server based on the Model Context Protocol (MCP) that supports machine condition monitoring and predictive maintenance via natural language interaction with Large Language Models (LLMs). The server constrains the LLM within an explicit perimeter of deterministic resources and tools for vibration-based diagnostics, including FFT spectral analysis with peak identification, envelope analysis for rolling element bearing defects, time-domain indicators, vibration severity assessment consistent with ISO standards and semi-supervised anomaly detection on extracted features. Each tool invocation produces structured outputs and artifacts that record inputs, parameters, and results. The LLM acts as an orchestrator that selects resources, configures parameters, invokes tools, and synthesizes conclusions anchored to computed evidence, thereby improving traceability and repeatability compared to unconstrained text-only interaction. End-to-end workflows are demonstrated in a reproducible package with code, examples, and demo data to support community-driven validation and extension toward industrial requirements. The software is archived on Zenodo and the GitHub repository serves as the collaboration hub. Full article

Rolling bearing faults originate from complex tribodynamic interactions among rolling elements, raceways, and the cage, yielding nonlinear, non-stationary vibration signals that are highly susceptible to noise and operating-condition variations, which compromises the reliability of diagnosis. To address this issue, this paper proposes the RConvNeXt–ECGA framework. The main contributions are twofold: (1) RConvNeXt is a convolutional module based on ConvNeXt, which achieves efficient multi-scale feature extraction through grouped parallel convolutions with multiple receptive fields; (2) Efficient Content-Guided Attention (ECGA) is a novel pixel-level attention mechanism, which adaptively reweights feature maps to highlight informative regions and suppress irrelevant interference. The proposed method achieves an average accuracy of 99.8% on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology, and 94.33% under cross-operating-condition tests, demonstrating superior robustness and generalization over representative deep learning-based baseline models. Full article

Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual Network (ResNet), and a Convolutional Block Attention Module (CBAM). First, a systematic multimodal representation selection framework is introduced, identifying the Markov Transition Field (MTF) as the optimal two-dimensional (2D) image modality due to its superior texture clarity and noise resistance compared to other methods. Second, parallel dual-branch architecture is designed to simultaneously process heterogeneous data. The 1D-GRU branch captures long-range temporal dependencies directly from raw vibration signals, while the 2D ResNet-CBAM branch extracts deep spatial features from the MTF images, adaptively focusing on key fault regions. These heterogeneous features are then fused through concatenation to retain complementary diagnostic information. Experimental validation on the Case Western Reserve University (CWRU) dataset demonstrates that the proposed model achieves a 99.57% accuracy in a 10-classification task. Furthermore, it exhibits significant parameter efficiency and outstanding robustness, with the accuracy decreasing by no more than 1.2% under noise interference and cross-load scenarios, comprehensively outperforming existing single-modal and advanced fusion methods. Full article

Rolling bearings are critical and vulnerable components in mechanical equipment and are prone to various types of damage during operation. Consequently, rolling bearing fault diagnosis is of significant engineering importance. In recent years, deep learning-based approaches have achieved considerable progress in intelligent bearing fault diagnosis. However, existing models still suffer from several limitations, including insufficient feature extraction under noisy conditions, limited diagnostic accuracy, high computational cost, and low operational efficiency. To address these challenges, an intelligent rolling bearing fault diagnosis method based on an ascending-dimensional convolutional neural network (ADCNN) is proposed. Compared with conventional neural networks, the proposed ADCNN features a more compact model size, improved noise robustness, and higher diagnostic accuracy. A large convolutional kernel is introduced in the first layer to enhance noise immunity, while an ascending-dimensional module is employed to reduce the number of network parameters and improve feature extraction capability. In addition, a reduced linear transformation layer (RLTL) is incorporated to further achieve a lightweight architecture. Experimental results on the Case Western Reserve University (CWRU) dataset and a self-designed test dataset demonstrate that the proposed ADCNN achieves superior fault diagnosis performance under different noise environments while maintaining computational efficiency and model compactness. Full article

Remaining useful life (RUL) prediction of rolling bearings is a crucial issue in intelligent predictive maintenance, thereby ensuring equipment safety and reducing maintenance costs. To address the challenge that traditional deep learning models struggle to simultaneously capture local temporal features and global degradation trends when processing degradation health indicators (HI), this paper proposes a hybrid RUL prediction model based on extended Long Short-Term Memory (xLSTM) and Transformer. The model employs an encoder–decoder architecture, integrating the Multi-Head Attention mechanism with the xLSTM module. This design simultaneously enhances the modeling capability of short-term dynamic features and effectively captures long-term degradation patterns. Validation was conducted on the XJTU-SY and PHM2012 datasets. The proposed model outperformed the comparative models across evaluation metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R²) and the Score, achieving a significant improvement in prediction accuracy and multi-dataset generalization capability. The proposed network provides a more accurate and generalizable solution for bearing health assessment and remaining useful life prediction and demonstrates significant potential for intelligent health management of industrial equipment. Full article

As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issue, a novel fault diagnosis method is proposed in this paper, which integrates an improved speed time-varying filtering empirical mode decomposition (ISTVF-EMD) with an adaptive sine–cosine optimization algorithm (A-SCA), enabling precise and efficient extraction of fault features. The core of the proposed method lies in improving the conventional time-varying filtering empirical mode decomposition (TVF-EMD) by setting a maximum decomposition layer limit, effectively addressing issues of excessive components and low computational efficiency during the decomposition of low signal-to-noise ratio (SNR) signals. Furthermore, a multi-characteristic frequency energy concentration centrality (MCFECC) index is employed as a fitness function to guide A-SCA in adaptively searching for the optimal bandwidth threshold and fitting order parameters of ISTVF-EMD, thereby extracting components with the most enriched fault information. Validated through simulation and multiple test bench cases, the results indicate that the proposed method can not only significantly enhance the fault characteristic frequencies and their harmonics in the envelope spectrum, successfully diagnosing outer race, inner race, and rolling element faults, but also, compared with the original method, ISTVF-EMD substantially reduces the computational time while ensuring or even improving the decomposition quality. The method presented in this paper provides an effective solution for achieving precise and adaptive fault diagnosis of rolling bearings under strong noise interference. Full article

The operational safety of rotating machinery heavily relies on the condition of its rolling bearings. However, under strong background noise and variable operating conditions, weak fault-induced impact responses are easily overwhelmed. To address these challenges, this paper proposes a dual-branch cross-modal fault diagnosis framework (STRN-CM) that integrates a Swin Transformer with a one-dimensional wide-kernel deep residual network (1D ResNet). The model develops a complementary structure of heterogeneous features. The enhanced 1D ResNet branch responds directly to the passage of volatile impulse features, which can detect early errors through raw vibrations. The Swin Transformer branch captures long-term periodic texture windows by using time–frequency images, which have an important dependence on time. Also, a Cross-Modal Attention Fusion (CMAF) scheme is introduced. Using high signal-to-noise ratio (SNR) temporal impulse features as query probes, the mechanism dynamically calibrates the response weights of time–frequency features, thereby achieving adaptive denoising and enhancement at the feature level. Experimental results demonstrate that STRN-CM achieves a diagnostic accuracy of 93.04% in harsh −6 dB noise conditions on the Case Western Reserve University (CWRU) dataset. Furthermore, it achieves a 97.99% accuracy on the Paderborn University (PU) dataset, showcasing superior generalization in cross-load and real fatigue damage transfer tasks. It also demonstrates significantly better generalization performance than single-modal networks in cross-load and real fatigue damage transfer tasks. Full article

To address the challenges of difficult feature extraction for rolling bearing vibration signals, low efficiency in optimizing diagnostic model parameters, and the tendency to get trapped in local optima, this paper proposes an improved ICEEMDAN feature extraction method based on Bayesian optimization and adaptive noise signal ratio enhancement (BAYES-ICEEMDAN-SNR) and combines it with the improved Coriolis force optimization algorithm (ITOC) to optimize the least squares support vector machine (LSSVM) fault diagnosis model. Firstly, Bayesian optimization is used to adaptively determine the noise parameters and introduce a dynamic signal-to-noise ratio adjustment mechanism to enhance the robustness of feature extraction; secondly, Chebyshev chaotic mapping, Cauchy mutation, and dynamic reverse learning strategies are applied to enhance the global search and local escape capabilities of ITOC, thereby optimizing the hyperparameters of LSSVM; and finally, the Keesey-Chestnut University bearing dataset and Huazhong University of Science and Technology gear dataset are used for verification. The experimental results show that the average fault identification accuracy of the proposed method reaches over 97%, which is superior to that of the comparison models, and the effectiveness of each core improvement module of the proposed model is verified through ablation experiments, providing an effective solution for intelligent fault diagnosis of rolling bearings and gears. Full article

As wind turbines trend toward larger sizes, higher rotational speeds, and extended service lives, higher demands are emerging for the dimensional accuracy, mechanical properties, and service reliability of the main shaft bearings. The hot bulging process is a critical process in bearing ring manufacturing. The stress–strain and dimensional evolution during the hot bulging process are crucial for the fatigue life and dimensional accuracy of rings with non-circular cross-sections. Therefore, based on the residual stress field from rolling as an initial condition, this paper established a coupled finite element model for the entire rolling-to-bulging process of GCr15SiMn bearing steel rings and verified the accuracy of the model. A stepwise rotation hot bulging process was innovatively designed. The stresses, strains, and deformation rates of the rings were thoroughly evaluated at different steps of the bulging process. Additionally, the effect of the bulge amount on the stress–strain uniformity and dimensional accuracy of the fabricated rings was also evaluated. Results indicate that based on the stepwise rotation hot bulging process conducted at 870–930 °C, when the first-step bulging amount is 1.50 mm, the secondary and third-step amounts are both 0.50 mm, and the bulging speed is 1.00 mm/s, while the roundness error of ring #3 stabilizes within 0.28–0.35 mm. The standard deviation of the axial equivalent strain was decreased by 92%, and the stress peak was also decreased by 39%. Above all, the stepwise rotation hot bulging process is an effective approach to improve the distribution uniformity of the stress–strain and the dimensional consistency of the bearing rings. This paper provides theoretical foundations and process guidance for the precision forming of large wind turbine bearing rings with non-standard sections. Full article

This paper addresses the challenge of extracting weak early fault signals from rolling bearings under variable speed conditions, where strong background noise often obscures diagnostic features. We propose a novel fault diagnosis method that integrates variational mode decomposition (VMD) and maximum second-order cyclo-stationarity blind deconvolution (CYCBD). The proposed approach begins by converting non-stationary vibration signals into angular-domain stationary signals using computed order tracking (COT). Subsequently, the parameters of the VMD algorithm are optimized via the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) to select the optimal modal components. A key contribution is the introduction of a composite index (CI), combining harmonic significance and the envelope spectrum crest factor, which serves as the fitness function for the SCSSA to optimize the critical parameters of CYCBD for enhanced feature enhancement. Finally, fault characteristics are extracted by analyzing the deconvolved signal with an order envelope spectrum. Both simulation and experimental results demonstrate the superior capability of the proposed VMD-CYCBD method in effectively identifying weak fault features submerged in strong noise under variable speed conditions. Full article

For optimizing the tribological behaviors of sliding bearing, an equal ratio structure of rectangular micro-grid is well constructed and then filled with the SnAgCu-CaF₂ (SC) to form a surface micro/nanostructure. The reciprocating wear tests are performed at different sliding frequencies, ensuring that the tribological property at 7 Hz of a TASC-G4 is the best. During wear, the SC in a grid structure migrates to the friction surface and then spreads out to form an SC-rich lubrication film. In this film, a good wrapping in SnAgCu of CaF₂ is ensured, helps a plastic enhancement of SnAgCu, an oxidation reduction and the rolling friction of CaF₂. These enhance the lubrication film to resist friction damage, reduce sliding resistance, and strengthen interface lubrication, subsequently improves the tribological behaviors of the TASC-G4. The methods and conclusions are obtained to provide an important reference for improving tribological adhibition of the sliding bearings. Full article