基于退化特征空间重构的多轴承健康寿命协同预测方法研究

为顺应现代制造业发展要求,做到视情科学维修,延长设备健康寿命。针对传统方法预测准确率较低的问题,采用多个轴承全生命周期数据为实验数据集,并选取均方根、峰值因子、峭度、频谱分区求和四个特征作为预测模型的输入值。另外,采用卷积神经网络构建预测模型,训练模型时采用学习率衰减机制,提高训练效率。实验表明,该方法相较于其他传统方法具有较高的预测准确率,可以对轴承健康寿命进行有效预测。

新型多轴承尾轴管及尾轴护管装置设计

针对传统尾轴管装置的弊端和不足,设计新型油润滑多轴承尾轴管装置、水润滑半封闭式多轴承尾轴管装置、敞开式舷外水润滑尾轴护管装置。阐述上述尾轴管装置的设计方案和技术特点,为中大型公务船的尾轴管装置方案设计提供参考。

多个低速重载轴承同轴不完全旋转状态下的振源识别

多个低速重载轴承同轴安装时,产生的故障振动脉冲会出现混叠现象,分别提出了利用振幅衰减原理和角域特征参量2种方法,解决了多轴承同轴不完全旋转状态下的振源识别问题,为脉冲信号的振源正确分离为低速重载轴承故障诊断方法研究奠定了基础。

角偏移的不确定性对主轴系统中角接触球轴承疲劳寿命的影响

研究在非理想安装条件下,轴承角偏移对主轴系统中轴承疲劳寿命的影响。为实现这一目的,首先介绍多轴承-主轴系统的分析模型,提出多自由度系统模型的求解方法,并对系统模型的有效性进行验证;其次,以四轴承-主轴系统为研究对象,考虑角偏移的不确定性,假设角偏移符合正态分布,基于系统模型分析不同主轴转速条件下,不同角偏移值时轴承内滚动体的接触力分布;最后,基于寿命理论,计算各轴承的疲劳寿命。结果表明:角偏移的不确定性可导致轴承寿命的显著分散性;对于系统中的预紧轴承,无角偏移时的疲劳寿命是角偏移存在条件下疲劳寿命的上限。随着主轴转速的降低,无角偏移时的轴承寿命与存在角偏移时的轴承寿命最小值的差异变得更大。

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

Demodulation spectrum analysis for multi-fault diagnosis of rolling bearing via chirplet path pursuit

The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.

Multiobjective scheduling optimization of bearing production workshop for green manufacturing

Aiming at the machining process of high-performance bearing parts,the green shop scheduling problem of bearing parts processing was studied herein,with the maximum completion time,minimum machine carbon emission,and minimum grinding fluid usage as the optimization objectives.The manufacturing process is divided into six technological processes:startup,clamping,machining,unloading,standby,and shutdown.The multiobjective green shop scheduling mathematical model is established.Then,an improved multiobjective genetic algorithm is proposed,adopting a segmented coding method that integrates the process and machine selections and improves the steps of crossover and mutation,all of which improve the algorithm s convergence.Finally,the bearing parts processing of a bearing company is taken as a case study,and large-scale data tests and analyses are constructed.The result shows that the proposed model can obtain lower completion time,carbon emission,and grinding fluid consumption,which verifies the scientificity and effectiveness of the proposed model.

Optimization of Grease Properties to Prolong the Life of Lubricating Greases

Grease life refers to the time it takes for the grease to lose its ability to keep the lubrication due to grease degradation. As grease life is generally shorter than fatigue life of bearing, the service life of grease-lubricated rolling bearings is often dominated by grease life. When designing a bearing systemolecular weightith grease lubrication, it is necessary to define the operating conditions limits of the bearing, for which grease life becomes a determining factor. Prolongation of grease life becomes an especially important challenge when the bearing is to be operated trader high-speed, high-temperature, and other severe conditions. Selecting a number of commercially sold greases composed of varying base oils, the author evaluated their properties and analyzed how each property affected the grease life by performing a multiple regression analysis. The optimum grease composition to best exploit each property was also examined. The results revealed among others that one would need to first determine the base oil type and then maximize ultimate bleeding while minimizing the evaporation rate.

A Study on the Levitation Control of the Multi-degrees-of-Freedom Rotational Machine Supported by Magnetic Bearings Using Flux Feedback

This paper deals with an open-loop characteristic of a magnetically levitated system including flux feedback. In order to design a controller to obtain a good disturbance rejection and to be insensitive to parameter variations, it might be useful to employ a flux feedback loop. The air gap flux which can be sensed by a proper sensor has linear relationship with respect to the change of the current and the air gap. This linear property decreases the inherent nonlinearity of the magnetic suspension system that is caused by the coupling between the electrical actuator and the mechanical plant. Simulation results achieved from a multi-degree-of-freedom numerical model show that the flux feedback loop makes an improvement of the performance of the magnetic suspension system against the load variations.