Investigations for vibration and friction torque behaviors of thrust ball bearing with self-driven textured guiding surface

Wu, Chunyang; Yang, Kai; Ni, Jing; Lu, Shuigen; Yao, Lei; Li, Xinglin

doi:10.1007/s40544-022-0627-4

Cited by 22 publications

(4 citation statements)

References 38 publications

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“…Notably, the results indicated that implementing a gradient groove texture could significantly reduce the friction torque of bearings. This texture facilitates a one-way self-driving function for liquid droplets, highlighting its potential for practical applications in bearing design [45]. Another 2023 piece of research explored the impact of various surface textures, including dimples, grooves, and gradient grooves, on thrust ball bearings' vibration and friction torque behaviours.…”

Section: State-of-the-art and Research Gapsmentioning

confidence: 99%

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2024

Machines

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This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

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Section: State-of-the-art and Research Gapsmentioning

confidence: 99%

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2024

Machines

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“…Rolling bearings represent essential constituents within rotating machinery, finding ubiquitous application across diverse sectors such as transportation, manufacturing, and construction [1][2][3]. Nevertheless, these bearings routinely endure substantial loads and undergo varying operational conditions, rendering them susceptible to an array of potential malfunctions, including wear, structural fissures, corrosion, and related issues.…”

Section: Introductionmentioning

confidence: 99%

Deep residual shrinkage networks with adaptively convex global parametric rectifier linear units for fault diagnosis

Zhang,

Zhang

et al. 2023

Meas. Sci. Technol.

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In response to the challenge posed by traditional deep learning methods, which apply uniform nonlinear transformations to all vibration signals and thus struggle to address fault diagnosis under variable working conditions, a novel activation function called the convex global parametric rectifier linear unit (CGPReLU) is developed based on our prior research. Initially, an analysis of the numerical patterns governing the adaptive derivation process of GPReLU’s two slope parameters revealed the surprising observation that these convex parameter combinations invariably appear in pairs. This discovery serves as the primary motivation for the development of CGPReLU. Leveraging this convex regularity, we subsequently redesigned a lightweight convex sub-network for the adaptive derivation of the CGPReLU’s slope. Simultaneously, a deep residual shrinkage network with CGPReLU is constructed for fault diagnosis. Furthermore, we introduce an innovative evaluation metric designed to measure the collective influence of diagnostic accuracy and computational complexity after the process of model lightweight. Finally, it is experimentally demonstrated that the developed method can maintain a better diagnostic performance while greatly improving the diagnostic efficiency under variable operating conditions compared to our previous work.

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“…In addition to grooved textures, other functional micro-textures also play an important role in friction reduction. To enhance the wear resistance of rolling bearings, Wu et al [ 17 ] studied three different liquid self-driven micro-textures on the surface of a bearing. It was found that the gradient micro-texture has the best effect in terms of reducing friction and suppressing vibration.…”

Section: Introductionmentioning

confidence: 99%

Study of the Friction Behavior of Embedded Fibers in YG8 Surface Grooves

Huang

Zhang

et al. 2023

Materials

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YG8 is a common cemented carbide material with excellent mechanical properties and mechanical properties, so it is widely used in the actual industry. However, due to the active chemical properties and strong affinity of tungsten alloy steel, it is easy to produce bonding and peeling in application, resulting in an unstable process and short service life. In order to control and reduce the surface wear of YG8 cemented carbide, groove-textured surface (GS) and flocking surface (FS) were prepared on smooth surface (SS). The friction characteristics of the samples were studied under different applied load conditions. The results show that the average friction coefficient of SS, GS and FS is inversely proportional to the load in dry/oil environment. Compared with SS, FS exhibits the lowest friction coefficient, which is reduced by 30.78% (dry friction) and 13.13% (oil lubrication). FS effectively improves the tooth jump phenomenon of the sample and the amplitude of the friction coefficient, friction force and load, and has the best anti-friction characteristics. At the same time, the FS with the fastest contact angle drop at any time also showed excellent wetting ability, and the wear rate decreased by an order of magnitude. The implantation of fibers in the groove inhibits the spalling and furrow of wear track, which is attributed to the effect of fibers on damage repair. In the friction process, FS increases the content of the O element and induces the formation of oxides. The friction mechanism is mainly chemical wear. The excellent tribological properties of FS have a good guiding significance and theoretical support for improving the tribological properties of high hardness material surfaces.

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Investigations for vibration and friction torque behaviors of thrust ball bearing with self-driven textured guiding surface

Cited by 22 publications

References 38 publications

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

Deep residual shrinkage networks with adaptively convex global parametric rectifier linear units for fault diagnosis

Study of the Friction Behavior of Embedded Fibers in YG8 Surface Grooves

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