Purpose -The purpose of this paper is to develop a good calculation model to accurately predict the lubrication characteristic of main bearings of diesel engine and improve the service life. Design/methodology/approach -Based on the coupling of the whole flexible engine block and the flexible crankshaft reduced by the Component Mode Synthesis (CMS) method, considering mass-conserving boundary conditions, the average flow model equation and Greenwood/Tripp asperity contact theory, an elastohydrodynamic (EHD)-mixed lubrication model of the main bearings for the diesel engine is developed and researched with the finite volume method and the finite element method. Findings -Obviously, the mixed lubrication of bearings is normal, while full hydrodynamic lubrication is transient. The results show that under the whole flexible block model, maximum oil film pressure, maximum asperity contact pressure and radial shell deformation decrease, while minimum oil film thickness increases. Oil flow over edge decreases, and so does friction loss. Therefore, coordination deformation ability of whole engine block is favorable to mean load. In the whole block model, friction contact happens on both upper shell and lower shell positions. In addition, average oil film fill ratio at the key position becomes smaller in the whole engine block model, and consequently increases the chances of cavitations erosion more. So, wearing resistance of both upper and lower shells and anti-cavitations erosion ability must be enhanced simultaneously. Originality/value -Based on the coupling of the whole flexible engine block and the flexible crankshaft reduced by the CMS method, considering mass-conserving boundary conditions, the average flow model equation and Greenwood/Tripp asperity contact theory, an EHD-mixed lubrication model of the main bearings for the diesel engine is built, which can predict the lubrication of journal bearings more accurately.
For the purpose of extracting the frictional vibration characteristics of the friction pair during friction and wear in different friction states, the friction and wear tests of friction pair in different friction states were conducted on a testing machine. Higher-dimensional fractal and multifractal characteristics hidden in time series can be examined by multifractal detrended fluctuation analysis (MFDFA) method. The frictional vibration time-domain signals, the friction coefficient signals and the frictional vibration frequency-domain signals were analyzed and multifractal spectra were acquired by using the MFDFA algorithm. According to the spectra, the multifractal spectrum parameters of these signals were calculated to realize the quantitative characterization of frictional vibration characteristics in different friction states. The analysis shows that it is symmetric in the variation trends of the multifractal spectrum parameters of the frictional vibration signals and the friction coefficient data. Based on the multifractal spectrum parameters of frictional vibration, the principal component analysis (PCA) algorithm was applied to establish the friction state recognition method. The results show that the multifractal spectra and their parameters can characterize the frictional vibrations, and the friction state recognition can be realized based on the multifractal spectrum parameters of frictional vibrations.Symmetry 2020, 12, 272 2 of 22 friction pair [7]. Therefore, the analysis of frictional vibrations is a better means to monitor the friction and wear states of the friction pair in real time during the operation of equipment.The study of the frictional vibration has attracted many scholars' interest. Jaeyong et al. studied the nonlinear behaviors of the frictional vibration by using spring-mass model based on the smooth friction velocity curve. The results show that the nonlinearity and instability of friction may bring forth chaotic frictional vibrations according to the friction curve [8]. Liu et al. showed that the cross correlation coefficient of frictional vibration is opposite to the variations of friction coefficient [9]. Sun D. et al. pointed out that the change law of the defined frictional vibration parameter k is consistent with the change trends of the friction coefficient during the tests [10]. Rouzic et al. studied the squeal noises of a wiper/windscreen contacts. Based on Stebeck's law of friction coefficient, they proved that noises are caused by self-excited frictional vibration [11]. Wernitz and Hoffmann's analysis indicated that irregular frictional vibration states of friction brakes are mainly dominated by intermittency phenomena [12]. Nadim et al. pointed out that the frictional vibration of dry friction has nonlinear characteristics, and the surface morphology of the friction pair change due to abrasive wear and adhesive wear at the interface of the friction pair results in the change of friction coefficient, which is the source of the nonlinear frictional vibration [13]. Recentl...
Purpose – This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles. Design/methodology/approach – In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk’s memory size, size of image samples, different inputting vectors and different classifiers were compared. Findings – The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems. Originality/value – This paper combined DTW algorithm with wear particle identification problem.
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