Analysis of Thermoelastohydrodynamic Performance of Journal Misaligned Engine Main Bearings

To understand the engine main bearings＇ working condition is important in order to improve the performance of engine. However, thermal effects and thermal effect deformations of engine main bearings are rarely considered simultaneously in most studies. A typical finite element model is selected and the effect of thermoelastohydrodynamic（TEHD） reaction on engine main bearings is investigated. The calculated method of main bearing＇s thermal hydrodynamic reaction and journal misalignment effect is finite difference method, and its deformation reaction is calculated by using finite element method. The oil film pressure is solved numerically with Reynolds boundary conditions when various bearing characteristics are calculated. The whole model considers a temperature-pressure-viscosity relationship for the lubricant, surface roughness effect, and also an angular misalignment between the journal and the bearing. Numerical simulations of operation of a typical I6 diesel engine main bearing is conducted and importance of several contributing factors in mixed lubrication is discussed. The performance characteristics of journal misaligned main bearings under elastohydrodynamic（EHD） and TEHD loads of an I6 diesel engine are received, and then the journal center orbit movement, minimum oil film thickness and maximum oil film pressure of main bearings are estimated over a wide range of engine operation. The model is verified through the comparison with other present models. The TEHD performance of engine main bearings with various effects under the influences of journal misalignment is revealed, this is helpful to understand EHD and TEHD effect of misaligned engine main bearings.

Characteristics of the Main Journal Bearings of an Engine Based on Non-linear Dynamics

Many simple nonlinear main journal bearing models have been studied theoretically, but the connection to existing engineering system has not been equally investigated. The consideration of the characteristics of engine main journal bearings may provide a prediction of the bearing load and lubrication. Due to the strong non-linear features in bearing lubrication procedure, it is difficult to predict those characteristics. A non-linear dynamic model is described for analyzing the characteristics of engine main journal bearings. Components such as crankshaft, main journals and con rods are found by applying the finite element method. Non-linear spring/dampers are introduced to imitate the constraint and supporting functions provided by the main bearing and oil film. The engine gas pressure is imposed as excitation on the model via the engine piston, con rod, etc. The bearing reaction force is calculated over one engine cycle, and meanwhile, the oil film thickness and pressure distribution are obtained based on Reynolds differential equation. It can be found that the maximum bearing reaction force always occurs when the maximum cylinder pressure arises in the cylinder adjacent to that bearing. The simulated minimum oil film thickness, which is 3 μm, demonstrates the reliability of the main journal bearings. This non-linear dynamic analysis may save computing efforts of engine main bearing design and also is of good precision and close connection to actual engine main journal bearing conditions.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

Numerical Simulation on Dynamic Load of Main Bearing of Tunnel Boring Machine Based on Combination Stratum

The load spectrum of the main bearing of tunnel boring machine( TBM) is difficult to establish because of the complex factors affecting the driving load of tunneling. In this paper, a simulation model of dynamic load of cutterhead is established,with a view to structural features and special conditions, based on a complex combination stratum, the cutter layout model and cutterhead control parameters,and it is a dynamic load boundary of the main drive bearing. Combined with the load distribution calculation of the main bearing and Hertz contact theory, the prediction model of dynamic load spectrum of the main drive bearing is completed during tunneling,and in accordance with the predicted results,the static and dynamics characteristics of load spectrum for the main drive bearing on the thrust and tilting moment are analyzed. The results of cutterhead load show that,in the certain complex stratum, the fluctuations of load for thrust rollers can reflect formation interface information of complex stratum in current tunneling. The main drive bearing bear the thrust and overturning moment of cutterhead under the composite,the external load has a greater influence on the load-spectrum of reverse thrust roller than that of main thrust roller,and the maximum contact stress of the two row roller is almost the same. The load spectrum,obtained by this method,can provide a meaningful reference for the design and checking of the main drive bearing,and also can be the basis of its fatigue reliability.

A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis

As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive.Currently,under conditions of strong noise and complex working environments,traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy.To address these issues,we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network(RDMA-WDAN)for TBM main bearing fault diagnosis.Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules,achieving better domain adaptation despite significant domain interference.The residual denoising component utilizes a convolutional block to extract noise features,removing them via residual connections.Meanwhile,the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features,concentrating on deep fault features.During training,a weighting mechanism is introduced to prioritize domain samples with clear fault features.This optimizes the deep feature extractor to obtain common features,enhancing domain adaptation.A low-speed and heavy-loaded bearing testbed was built,and fault data sets were established to validate the proposed method.Comparative experiments show that in noise domain adaptation tasks,proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%,23.088%,43.133%,16.344%,5.022%,and 9.233%over dense connection network(DenseNet),squeeze–excitation residual network(SE-ResNet),antinoise multiscale convolutional neural network(ANMSCNN),multiscale attention module-based convolutional neural network(MSAMCNN),domain adaptation network,and hybrid weighted domain adaptation(HWDA).In combined noise and working condition domain adaptation tasks,the RDMA–WDAN improves the accuracy by 45.672%,23.188%,43.266%,16.077%,5.716%,and 9.678%compared with baseline models.