Slewing bearing

About: Slewing bearing is a research topic. Over the lifetime, 1498 publications have been published within this topic receiving 5309 citations.

Multi-row ball type slewing bearing and machining method thereof

Abstract: The invention discloses a multi-row ball type slewing bearing and a machining method thereof. The multi-row ball type slewing bearing comprises an outer ring 1, an inner ring 2 and steel balls 3 and is characterized in that the inner annular surface of the outer ring 1 is provided with at least three rows of inner raceways at intervals according to a certain size; the outer annular ring of the inner ring 2 is also provided with at least three rows of raceways which correspond to the inner raceways of the outer ring; the inner raceways and the outer raceways are in the same shape, and corresponding raceways form complete steel ball raceways; and the steel balls 3 are evenly distributed in each row of steel ball raceway and separated by insulating blocks 6 or solid retainers 7. The multi-row ball type slewing bearing has three or over three rows of bearing rollers, thereby having high bearing capacity. Meanwhile, because the slew bearing needs less materials due to compact structure, thereby having good economic performance. The multi-row ball type slewing bearing is suitable for working places requiring the slewing bearing with high bearing capacity and good rigidity and limited in mounting space of the slewing bearing.

Slewing bearing quenching type inner gear ring processing technology

Abstract: The invention relates to a slewing bearing quenching type inner gear ring processing technology The technology processes comprise workblank roll forming, rough turning, half finish turning, rolling path quenching, finish turning I, gear producing, gear tooth quenching, finish turning II, finish turning III, rolling path polishing and mounting hole drilling The processing technology is characterized in that the stage of the finish turning III comprises the following steps: placing the bottom surface of a workpiece on a contour cushion of a worktable, rectifying through an excircle positioned at the lower part of a rolling path, performing finish turning on the end surface and the excircle positioned at the upper part of the rolling path, and processing the size H, and the size D of the excircle positioned at the upper part of the rolling path to the size required by a drawing Through the adoption of the technology, the content of the finish turning III process is enlarged after a gear tooth is quenched, the parallelism requirement of the bottom surface (the standard plane) of the workpiece and the end surface (a non-standard plane) of the workpiece are ensured, and reliable processing standard guarantee can be provided for the follow processes, so that the precision of slewing bearing is improved, the mounting and application of the slewing bearing product can be ensured, and then the service life of the slewing bearing product and a connecting bolt can be prolonged

A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings

Abstract: Slewing bearings are vital functional components of large machinery. It is of far reaching significance to study their life prediction and health management. Many studies are based on data-driven approaches. However, part of them in the form of “black-box” lack actual physical meanings due to opacity model structures and have difficulty in choosing optimal parameters. Few kinds of literature focus on explicit model relationships for slewing bearings' life models. In this paper, a novel approach based on symbolic regression is proposed with the aim of exploring slewing bearings' explicit life models in depth and to predict residual useful life (RUL). The proposed method integrates the strengths of multiple signals describing a comprehensive response to slewing bearings' health and various genetic programming (GP) algorithms modeling life expressions. In addition, independent, hybrid, and piecewise strategies are introduced and explicit model relationships with respect to degradation indicators (DIs) are established via GPs. To verify the proposed method, three run-to-failure experiments under discrepant operating conditions of slewing bearings are carried out. Prediction results demonstrate that models generated by epigenetic linear genetic programming (ELGP) under hybrid and piecewise modeling strategy with similarity-based combination strategy perform best. More importantly, their life expressions are more succinct and intelligible than in other situations.

MDCCS Based Multistage Life Prediction of Slewing Bearing with a Novel Performance Description: an Improved Variational Mode Decomposition Approach

Abstract: Residual Useful Life (RUL) prediction plays a vital role in the domain of rotating machine condition monitoring. There have been extensive researches on the small bearings’ RUL prediction. Meanwhile, it is relatively less for slewing bearings which are used as connections between slewing systems and endure heavy load and harsh working conditions, its reliability of operation guarantees the safety of whole machines. Traditional condition monitoring of rotating machine mainly relies on vibration signal, slewing bearings’ own unique characteristics such as: large size, low speed, heavy load and complicated degradation process make life prediction very difficult and quite different from small bearings. Although many scholars have devoted their efforts to developing suitable slew bearings’ life prediction models, few have taken their intrinsic characteristics into consideration. To address this issue and improve the precision of RUL prediction, this paper first proposes High Dimensional Mode Signal Group (HDMSG) which is generated by Multi-Source Optimized Variational Mode Decomposition (MSOVMD). It is designed to contain Intrinsic Mode Functions (IMF) from diverse vibration signals to make a more comprehensive description of slewing bearings’ degradation and enhance damage signal. Degradation features extracted from HDMSG, temperature and torque signal are selected and fused afterwards. Secondly, considering the complex damage or degradation of slewing bearings, Maximum Differential Coefficient-Clustering Segmentation (MDCCS) is proposed and introduced as Health Stage (HS) division method which takes both advantages of multi-physical signals’ trend and clustering algorithm. It is worth noting that two accelerated run-to-failed experiments under completely different loading process were conducted for verification. Comparisons demonstrate the superiority of the alternatives of vibration signal: HDMSG and MDCCS based HS division methods.

Slewing bearing structure

Abstract: There is provided a slewing bearing structure capable of maintaining favorable bearing performance by adjusting and changing rigidity thereof while minimizing the increase in the weight thereof for preventing distortion of a pressure pattern caused by structural deformation from unfavorably affecting the bearing performance. A slewing bearing of a rolling bearing, in which a rolling element is put between bearing rings and formed on an inner ring and an outer ring, has a slewing bearing structure in which a rigidity strengthening portion, at which rigidity of the inner ring and/or the outer ring is increased more than that at peripheral portions, is formed at a circumferential area where a bearing contact pressure is high.