Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

Fatigue Life of Slewing Bearings under Combined Radial,Axial and Tilting Moment Loads

A calculation method of fatigue life for slewing bearings under combined radial, axial and tilting moment loads was proposed. Single row four-point contact ball slewing bearing being used as a case, the statics model of the slewing bearing was established and a set of equilibrium equations were obtained. By solving the equilibrium equatioas, the rolling element loads were obtained and the equivalent rolling element loads were calculated further. By using the geometrical parameters of the bearing, the rating rolling element loads were calculated, and the fa- tigue life of the bearing was calculated by using the rating rolling element loads and the equivalent rolling element loads. A calculation example shows the feasibility of the proposed method.

Methodology for the assessment of the friction torque of ball slewing bearings considering preload scatter

This manuscript presents an innovative methodology for the assessment of the friction torque of ball slewing bearings.The methodology aims to overcome the limitations of state-of-the-art approaches,especially when the friction torque is conditioned by the preload of the balls.To this end,the authors propose to simulate the preload scatter when solving the load distribution problem,prior to the friction torque calculation.This preload scatter allows to simulate a progressive transition of the balls from a four-point contact state to a two-point contact one.By implementing this capability into an analytical model,the authors achieve a successful correlation with experimental results.Nonetheless,depending on the stiffness of the structures to which the bearing is assembled,it is demonstrated that the rigid ring assumption can lead to inaccurate friction torque results when a tilting moment is applied.The methodology described in this research work is meant to have a practical application.Therefore,the manuscript provides guidelines about how to use and tune the analytical model to get a reliable friction torque prediction tool.

Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.