Search Results (27)

The operational health state of motor bearings is critical to the operational safety of harbor portal slewing cranes. However, in harsh industrial environments with strong noise and time-varying rotational speeds, existing bearing fault diagnosis methods still suffer from the problems of incomplete fault feature extraction from single-sensor signals and the excessively large size of multi-source fusion models, which makes them unable to adapt to edge deployment. To address these issues, this paper proposes a Multi-source Feature Fusion Lightweight Network (MTFL-Net) integrated with targeted structured channel pruning. First, vibration and current signals are preprocessed via differentiated time-frequency transformation and converted into 2D time-frequency images, to fully preserve transient impact and spectral fault features. Second, a multi-branch feature extraction architecture embedded with residual connections, multi-scale convolution and channel attention gating is designed, to alleviate feature degradation and adaptively enhance fault-sensitive features. Third, targeted structured channel pruning is performed on the feature extraction branches, to remove redundant channels while retaining the multi-source fusion logic and core feature extraction structure. Experiments on two public bearing datasets show that the original model achieves 99% diagnostic accuracy, and the pruned model still maintains an accuracy of 95%. The results demonstrate that MTFL-Net can significantly reduce model size and computational cost while retaining high diagnostic precision. Full article

To investigate the load-bearing characteristics of lightweight surface-mounted slewing cable anchorage, this paper takes the Yellow River Three Gorges Bridge project as an example, establishing a nonlinear finite element model and verifying its effectiveness through a 1:100 scale physical model test. Furthermore, a theoretical stability analysis model was established to quantify the contributions of base friction and toothed block clamping action. By analyzing displacement behavior, rock mass shear characteristics, and plastic zone evolution, the combined load-bearing mechanism was revealed. The results show that the anchorage system begins to destabilize when the load reaches 18P. Both numerical and theoretical analyses confirm that the toothed blocks significantly improve the stability of the anchorage system; the safety factor increases from 6.84 considering only friction to 16.59 considering clamping action, which is consistent with the 17P plastic threshold observed in the simulation. Rock mass resistance is generated from bottom to top, providing passive resistance through shear action. The final determined failure mode is the interconnection of local plastic zones and the overturning failure of the anchorage system. 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

Excavators are critical equipment in mining, construction, and other fields. The four-point contact slewing bearings used in their slewing mechanisms operate under harsh conditions such as heavy loads and impacts. Furthermore, the bearing rings are prone to elliptical deformation after installation, making them susceptible to premature failure. To address this issue, this paper establishes a mechanical bearing model to investigate the load distribution among balls and the fatigue life of the bearing under elliptical deformation of the rings. It systematically analyzes the influence of key design parameters. The research finds that elliptical deformation of the rings leads to contact angle deviation and a reduction in load-bearing balls, resulting in severe degradation of bearing fatigue life; therefore, its occurrence must be strictly controlled. Designing with a groove curvature radius coefficient within the range of 0.51 to 0.52 achieves an optimal balance between fatigue life and the four-point contact geometry of the balls. There exists an “optimal clearance” that maximizes bearing fatigue life; when considering significant elliptical deformation, the clearance design should be appropriately increased. Increasing the design contact angle enhances load capacity and helps mitigate the effects of elliptical deformation. However, an excessively large contact angle can cause ellipse truncation in the raceway contact zone; thus, the contact angle should be designed based on practical conditions. Increasing the number of balls can improve the influence of ovality on load distribution and enhance the bearing’s fatigue life. This study provides a theoretical reference for the design of high-reliability slewing bearings for excavators. Full article

Internal wall grinding of pipes constitutes a critical pretreatment procedure in the anti-corrosion repair operations of Prestressed Concrete Cylinder Pipes (PCCP). To address the limitations of low efficiency and poor safety associated with traditional manual internal wall grinding in PCCP anti-corrosion repair, this study presents the design of a support-wheel-type internal wall grinding robot for pipes. The robot’s structure comprises a walking support module and a grinding module: the walking module employs four sets of circumferentially equally spaced (90° apart) independent-support wheel groups. Through an active–passive collaborative adaptation mechanism regulated by pre-tensioned springs and lead screws, the robot can dynamically conform to the inner wall of the pipe, ensuring stable locomotion. The grinding module is connected to the walking module via a slewing bearing and is equipped with three roller-type steel brushes. During operation, the grinding module revolves around the pipe axis, while the roller brushes rotate simultaneously, generating a composite three-helix grinding trajectory. Mathematical models for the robot’s obstacle negotiation, bend traversal, and grinding motion were established, and multi-body dynamics simulations were conducted using ADAMS for verification. Additionally, a physical prototype was developed to perform basic functional tests. The results demonstrate that the robot’s motion characteristics are highly consistent with theoretical analyses, exhibiting stable and reliable operation, excellent pipe traversability, and robust driving capability, thus meeting the requirements for internal wall grinding of PCCP pipes. Full article

This study investigates how the clarity of frequency-domain characteristics in vibration signals affects the performance of deep learning models for bearing fault classification. Two datasets were used; these were the CWRU benchmark dataset, which exhibits distinct and easily separable spectral signatures across fault modes, and a custom low-speed bearing dataset in which small defects do not significantly alter the frequency spectrum. To enable a clear and interpretable comparison, simplified CNN and LSTM architectures with a single core layer were deliberately employed. This design choice allows performance differences to be attributed directly to the inherent learning mechanisms of each architecture rather than to model complexity. Representation analysis shows that LSTM-F achieves the highest accuracy when the dataset contains clearly distinguishable spectral patterns, as in the CWRU case. In contrast, CNN-S outperforms both LSTM models in the experimental dataset, where fault-induced frequency characteristics are weak or ambiguous. Additional representation analyses further reveal that LSTM-F relies on consistent frequency-indexed patterns, whereas CNN-S captures more complex time–frequency interactions, making it more robust under low-separability conditions. These findings demonstrate that the optimal deep learning architecture for bearing fault classification depends on the degree of frequency separability in the data. LSTM-F is preferable for severe faults with distinct spectral features, while CNN-S is more effective for minor defects or systems exhibiting complex, weakly discriminative frequency behavior. Full article

We aimed to address the issue of fretting wear on the rollers and raceways of pitch bearings in wind turbines during shutdown and under intermittent high loads. This study focuses on triple-row cylindrical roller bearings. A finite element wear simulation of the contact area between a single roller and the raceway was established based on Hertzian contact theory and the modified Archard model. The wear coefficient values of the model before and after coating were verified through experiments, with results of k₁ = 3.125 × 10⁻⁸ and k₂ = 4.5 × 10⁻¹⁰, respectively. The effects of normal load, displacement amplitude, and cycle number on the fretting wear behavior of rollers under both uncoated and GLC-coated conditions were investigated. The results show that the GLC (Glassy Carbon-like Carbon) film significantly reduces the friction coefficient and wear. Compared to uncoated rollers, it reduces the maximum wear depth by approximately 90.53% across various normal loads, displacement amplitudes, and numbers of cycles. Additionally, the wear rate of the coated rollers remains consistently low with small fluctuations. The conclusion holds that the GLC film reduces the interface shear force and effective slip amplitude, enhances surface hardness and stability, and improves the fretting wear resistance of pitch bearings by an order of magnitude under complex load and oil-starved conditions. The primary objective of this work is to investigate the mechanisms for enhancing the anti-fretting wear performance of pitch bearings, with the goal of significantly extending their service life and reliability in harsh operating environments. Full article

Oscillating grease-lubricated slewing bearings are used in several applications. One of the most demanding and challenging is the rotor blade bearings of wind turbines. They allow the rotor blades to be turned to control the rotational speed and loads of the complete turbine. The operating conditions of blade bearings can lead to lubricant starvation of the contacts between rolling elements and raceways, which can result in wear damages like false brinelling. Variable oscillating amplitudes, load distributions, and the grease properties influence the likelihood of wear occurrence. Currently, there are no methods for rating this risk based on existing standards. This work develops an empirical methodology for assessing and quantifying the risk of wear damage. Experimental results of small-scale blade bearings show that the proposed methodology performs well in predicting wear damage and its progression on the raceways. Ultimately, the methods proposed here can be used to incorporate on-demand lubrication runs of pitch bearings, which would make turbine operation more reliable and cost-efficient. Full article

To investigate four-point contact ball slewing bearings, a bearing support bolt-integrated model was created with HyperMesh and ANSYS software, and its accuracy was theoretically confirmed. This study examines how the rolling element number Z, contact angle α, bolt number N, bolt pre-tightening force coefficient P, and radial load-overturning moment angle θ affect the comprehensive performance of four-point contact ball slewing bearings and connecting bolts. The study found that increasing Z, α, N, P, and θ reduces overall bearing, ring, rolling element, and contact load deformations. The maximum deformation and stress of bolts rise with P but decrease with Z, α, N, and θ. The degree of influence of each parameter on the deformation of the inner and outer rings, the deformation of the rolling element, and the contact load of the rolling body from large to small is ranked as follows: α, N, Z, θ, and P; the degree of influence on bolt deformation and bolt stress distribution uniformity from large to small is ranked as follows: N, α, Z, θ, and P; the degree of influence on the overall deformation of the bearing from large to small is ranked as follows: N, θ, α, Z and P; the degree of impact on the maximum stress of the bolt from large to small is ranked as follows: P, N, Z, α, θ. To improve the overall performance of a four-point contact ball slewing bearing, increase α, N, Z, and θ. Full article

The pitch bearings of wind turbines are slowly oscillating, grease-lubricated slewing bearings. They facilitate the pitching movements of blades which control aerodynamic loads. These bearings have diameters of several meters, their blade-side sealings can face the environment, bending moment loads can cause radial deformation of the bearing rings, and their highly variable operating temperatures can facilitate condensation of water inside them. All of this makes water ingress into the lubricant possible. There is limited public knowledge with regards to the maximum water content for safe operation in this application. This work presents the results of a series of scaled wind turbine time series tests with both ‘dry’ (no water contamination) and ‘wet’ (10 mass % demineralized water added) greases. A set of four commercially available greases were tested. The time series were scaled from wind turbine operation and represented a 13.7 h worst-case scenario of operation with small oscillation amplitudes and no longer lubrication runs in between. Three of the greases showed reduced friction and no or limited raceway damage in the wet condition, whereas one showed increased friction and raceway damages. Full article

Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios. Full article

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance. Full article

The present study was conducted to evaluate the analytical precision of finite element analysis models of wind turbine bearings. In the finite element analysis models, balls were modeled as finite element meshes as a solid model or replaced by nonlinear springs as two kinds of spring models. In addition, test bench modeling was performed to calculate the displacement following the application of a turnover moment by means of a global model analysis and to calculate the contact stress by means of a sub-model analysis. The comparison of the results of the finite element analyses with the results of the bearing bench test showed that the analytical precision was 17% in the single-spring model, 9% in the Daidie spring model, and 3% in the finite element mesh ball model, indicating that the finite element mesh ball model exhibited the highest precision. Full article

Typical radial active magnetic bearings are structurally symmetric. For example, an eight-pole bearing uses two opposing pairs to control one axis by winding the pair in series. The magnetic force produced by an active magnetic bearing is quadratically proportional to coil currents and inversely proportional to the square of the gap between the bearing and the journal. Bias linearization is widely used to linearize the relationship of coil currents to the magnetic force. However, the bias currents increase ohmic losses and require a larger than necessary capacity of power amplifiers to supply the sum of bias and control currents. Unbiased control of symmetric bearings has the critical issue of slew-rate limiting. Unbiased control of unsymmetrical bearings can eliminate the need for bias currents while avoiding slew-rate singularity except in extreme cases. Although a generalized inversion of the force–current relationship of unbiased unsymmetrical bearings has been proposed previously, no experimental validation is reported. The main objective of this research is to implement the unbiased control strategy and show that exactly the same linear control strategy for eight-pole symmetric bearings can be applied to nine-pole unsymmetrical bearings on industry-scale compressor test rigs. Two test rigs are built: one with eight-pole symmetric bearings and another with nine-pole unsymmetrical bearings. Linear control algorithms are designed and applied. Both control algorithms are linear and consist of lead filters and notch filters. The test results show that the linear control design used for unsymmetrical bearings can achieve the same level of stability that the symmetric bearings provide, satisfying the sensitivity criterion specified by ISO 14839-3. Full article

Many factors affect the accuracy of the estimation of the remaining useful life (RUL) of the fan slewing bearings, thereby limiting the sustainable development of the wind power industry. More specifically, the traditional vibration data, which are easily disturbed by noises, cannot comprehensively characterize the health status; thus, the physical model is difficult to establish, and when the data-driven model analyzes the status, it results in unclear physical mechanisms. A new nonlinear Wiener degradation model was established based on the fusion of the physical models and the data-driven models, which was employed to characterize the degradation process of the slewing bearings in this work, and for the local temperature distribution, which has a strong anti-interference ability, the multi-sensor temperature data fusion was selected to analyze the RUL prediction. First, the multi-sensor temperature data were fused by performing a principal component analysis (PCA) to obtain the comprehensive health index (CHI), which represents the fan slewing bearings. Second, the Arrhenius Equation, which characterizes the degradation using temperature, was introduced into the nonlinear Wiener model, and a new degradation model was established. Moreover, considering the random change of the drift coefficients and the individual differences, the closed expression of the probability density function (PDF) of RUL was derived. Third, maximum likelihood estimation (MLE) was used to estimate the parameters. In addition, Bayesian analysis was used to update parameters to achieve real-time estimation. The results demonstrated that the proposed method can be used to significantly improve the fitting degree of the model and the accuracy of RUL estimation. Full article