Main Bearing Research Articles

Mixed elastohydrodynamic analysis of a coupled journal-thrust bearing system in a rotary compressor under high ambient pressure

The hermetic rotary compressor's bearings, including the thrust bearing, the main and sub bearings, work together under high ambient pressure. The thrust bearing works under light axial loads, but sever wear is found, which may be related to the crankshaft's misalignment and deformation and the high ambient pressure. To investigate this phenomenon, a complete coupled journal-thrust bearing model is constructed. The bearing forces are calculated with the mixed elastohydrodynamic model under high boundary pressure. The effects of the deformations and ambient pressure on the bearing performance are investigated. Results show that ignoring the crankshaft deformation makes the simulation underestimate the thrust bearing wear, and the high ambient pressure is the leading cause of the thrust bearing wear.

One‐step preparation of carbon fiber‐ZrO2 hybrid and its enhancement on the wear‐resistant properties of polyimide

AbstractThe carbon fiber‐ZrO2 (CF‐ZrO2) hybrid was prepared through one‐step hydrothermal method, which well realized the synergistic enhancement of CF and ZrO2 nanoparticles on the wear‐resistant performance of polyimide (PI) composites. Results showed that the ZrO2 nanoparticles were tetragonal and monoclinic mixed phase. The average particle size was about 10 nm. And they were in‐situ synthesized on the surface of CF, which endowed CF with much rougher surface and further improved the interfacial adhesion of PI composite. Moreover, the CF‐ZrO2 hybrid reinforced PI composite possessed higher shore hardness and thermal stability than those of PI or its composite reinforce with single CF because of CF with good compressive strength and ZrO2 with outstanding thermal property. In addition, the anti‐wear properties of the PI were greatly improved by filling CF‐ZrO2 hybrid, which was mainly because CF as the main bearing phase of the friction surface of the composites could bear most of the stresses onto the worn surface and protect the polymer matrix from severe wear in the sliding process. Meanwhile, the ZrO2 nanoparticles presented at the interface of CF and PI could not only carry the applied load, but also enhance the interfacial combination of CF with PI to form a good stress transfer and prevent the failure of CF, finally improving tribological properties of composites. Besides, the PI/CF‐ZrO2 kept reasonable friction and wear performance under high loads and rotating speeds.

Methodology for the systematic design of conical plain bearings for use as main bearings in wind turbines

With the possibility to replace sliding segments on the tower without disassembling the drivetrain, the use of segmented plain bearings with conical sliding surfaces as main bearing in wind turbines has a great potential to reduce the maintenance costs and thus the levelized cost of energy (LCOE). Furthermore, the short axial design leads to lower investment costs. Since this design is totally new and no design guidelines are available so far, the objective of this paper is to investigate the influence of the geometric parameters on the hydrodynamic pressure distribution of the bearing. In this context a parameter screening is performed using a suitable test field according to Plackett and Burman in order to determine the most relevant parameters. With the help of the simulations carried out after this test field, correlations between the geometric parameters and the hydrodynamic pressure distribution are evaluated. To be able to quantitatively analyze the three-dimensional pressure distribution, several key values are defined in this paper that describe the pressure distribution. The content of this paper is part of a methodology with the goal of developing a design guideline for conical plain bearings.

Seismic Design Optimization of Steel Frames with Steel Shear Wall System Using Modified Dolphin Algorithm

The first step in any successful design of a structure is selecting the exact and accurate designing method. Designing a structure most economically (i.e. materials such as steel and concrete) by satisfying seismic and gravity criteria based on codes of practice is a complicated procedure that requires not only technical engineering knowledge but also years of experience. Thus, this paper presents an efficient and reliable approach to obtain the optimum design in steel frames with shear walls as the main seismic bearing component. The main objective is to use a modified dolphin echolocation algorithm with a multi-variability ability approach in achieving the optimized results. This approach represents the best positioning option for steel shear walls in elevations, spans, and dimensions of the elements. According to modifications applied to the algorithm, the computational time decreases, and the results are highly accurate. The designed structures with the minimum amount of steel satisfy all the requirements in the seismic design and steel structures design code, and the results of this work are entirely efficient. This efficiency is illustrated by three numerical examples. The results illustrated the superiority of the metaheuristic approach over the standard procedure. Besides, considering the weight of shear wall for the first time led to the 10% reduction of the weight of the structure.

Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis

AbstractAs a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber‐physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA‐RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co‐design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA‐RNN can improve model performance. Meanwhile, DDA‐RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing.

A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.

Вплив відцентрових сил на змащування підшипників колінчастого вала в аварійних режимах роботи двигуна автомобіля

The analysis of the crankshaft bearing condition of the automotive internal combustion engines in the case of insufficiency and breakage of oil supply to them is carried out. It is noted that this fault is one of the most common causes of damage to rubbing pairs in operation. At the same time, the different groups of bearings are often damaged, which cannot be explained within the framework of existing models of plain bearing lubrication. The objective of the work is to develop a mathematical model of oil supply to connecting rod bearings in emergency mode, taking into account the characteristic features of the bearing design. The model also, depending on the nature of the damage, should help to determine and explain the causes of bearing failures if they occur in different modes when operating conditions are broken. A computational model has been developed that makes it possible to assess the effect of design differences in the features of oil supply and the action of the centrifugal forces during crankshaft rotation on the oil column in the lubrication hole where oil is supplied to the conrod bearing. Calculations of the change in time of the oil supply pressure to the connecting rod bearings for the various designs of the crankshaft lubrication holes have been performed. It is shown that, depending on the operating mode of the engine and its design, the oil pressure in front of the connecting rod bearings does not disappear immediately after oil supply failure to crankshaft. Moreover, the lower the crankshaft speed is, the longer the lubrication of the conrod bearings will continue. The calculation results are confirmed by the data of the expert studies of the engine technical condition, in which the crankshaft was wedged in the damaged main bearings was found in the absence of serious damage to the connecting rod ones. It has been found that such features of the damage correspond to an rapid breakage of the oil supply to the crankshaft in the case of such operational damage as the oil pump and pressure reducing valve failure, the oil filter seal and oil pan destruction, etc. The developed model explains the difference in lubrication conditions and in the damage feature to the main and connecting rod bearings in the emergency cases of the oil supply breakage, which are observed during operation, and helps to clarify the failure causes. This makes it possible to use the model and the obtained data when providing auto technical expert studies of the failure causes of automobile internal combustion engines This makes it possible to use the model and the obtained data when providing auto technical expert studies of the failure causes of automobile internal combustion engines when the operating conditions are broken.

Optimal Condition-based Opportunistic Maintenance and Spare Parts Provisioning for a Two-unit System using a State Space Partitioning Approach

Making optimal decisions on preventive maintenance (PM) and spare parts provisioning for a multi-unit system is a practical problem in many industrial applications. In practice, decision makers must deal with a high level of complexity because of complex interactions among multiple units. In this work, we study optimal condition-based opportunistic maintenance (CBOM) and spare parts provisioning policies for a two-unit system experiencing continuous deterioration. A state space partitioning approach considering the deterioration states of the system along with the spare parts inventory state is proposed to analyse the requirements and actions for different maintenance types under the constraints of spare parts and those for spare parts holding and ordering. The explicit representations of stationary probability densities of combined states and their numerical solutions are deduced to obtain the corresponding probabilities of interest. Based on the results and the semi-regenerative process theory, an expected long-run cost rate model is developed to determine the optimal joint strategy. To illustrate the use of the proposed approach in practice, a wind turbine system consisting of main bearings and gearboxes is studied. The numerical results and sensitivity analysis validate the proposed approach and its practical values in solving such complex industrial problems.

Constructing fast and representative analytical models of wind turbine main bearings

Abstract. This paper considers the modelling of wind turbine main bearings using analytical models. The validity of simplified analytical representations used in existing work is explored by comparing main-bearing force reactions with those obtained from higher-fidelity 3D finite-element models. Results indicate that there is good agreement between the analytical and 3D models in the case of a non-moment-reacting support (such as for double-row spherical roller bearings), but the same does not hold in the moment-reacting case (such as for double-row tapered roller bearings). Therefore, a new analytical model is developed in which moment reactions at the main bearing are captured through the addition of torsional springs. This latter model is shown to significantly improve the agreement between analytical and 3D models in the moment-reacting case. The new analytical model is then used to investigate load characteristics, in terms of forces and moments, for this type of main bearing across different operating points and wind conditions.

Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection

Field failures of wind turbine main bearings yield to undesired downtime and significant maintenance costs. Fatigue failure is a dominant mode for legacy turbines, which can be expressed with physics-informed models to some extent. However, these models often inherent large uncertainties due to unknown lubricant degradation mechanism. Therefore, periodical assessment of the grease state plays a crucial role in calibration of bearing fatigue models. As opposed to detailed laboratory analysis, grease visual inspection can lead to large uncertainties in characterization of grease condition (although visual inspection can be cost and time effective). In this paper, we introduce a hybrid model for main bearing fatigue damage accumulation and calibrated using only visual grease inspections. In our hybrid model, bearing fatigue damage portion consists of known physics formulations, and unknown grease degradation is represented with deep neural networks. In addition, we introduce a custom tailored classifier that enables the model to map from damage scale to visual rankings. Results showed that the bearing fatigue prognosis model can be successfully calibrated, even with limited and noisy field observations. Moreover, the model can help optimizing park reliability by suggesting turbine-specific regreasing intervals. The source codes and links to the data can be found in the following GitHub repository https://github.com/PML-UCF/pinn_wind_bearing.

Mechanical Loading Model of Main Bearing of Wind Turbine Based on Test Bench

Wind-turbine main bearing has to withstand dynamic loads with different directions and different magnitudes in complex environments, and its stable operation has a vital impact on the performance of the entire wind turbine. Therefore, the fatigue strength test of wind turbine bearing is helpful to ensure the normal operation of the whole wind turbine. According to the actual force of main bearing in natural environment, this paper designed a large-scale wind-turbine bearing test bench to detect the deformation performance of the bearing. Through the establishment of the mechanical loading model, the simulation of the actual working conditions of main bearing is realized by the loading of the eight hydraulic cylinders of the test bench, and the radial and axial displacement of the test bearing under different wind conditions are recorded by displacement sensors. A number of temperature sensors are used to monitor the real-time temperature change of the bearing inner ring. The test results show that the loading effect of eight hydraulic cylinders can realize the force of wind turbine bearing under wind load, and the test bench can effectively detect wind-turbine bearings with a diameter of 2.5 m. The mechanical loading method and test results can provide guidance for further inspection of the wind-turbine bearing.

Research on Life Prediction of Fan Spindle Bearing Based on Gated Recurrent Unit Neural Network

In order to solve the problems of redundancy calculation and inefficiency of traditional machine learning algorithm in dealing with large amount of historical data of fan, a new predictive algorithm based on gated recurrent unit (GRU) is proposed to predict the remaining service life of fan spindle bearing. Firstly, the vibration history data of the main shaft bearing of the fan is analyzed to find out the relationship between the characteristic value and the remaining life, and the characteristic parameters which can reflect the remaining life are selected; Then, GRU is used to build the remaining service life prediction model of spindle bearing, and the main parameters of the model are adjusted to improve the prediction accuracy of the model. Compared with long short term (LSTM) algorithm, GRU is an effective tool to deal with a large number of data sets.

Extended failure mode and effect analysis approach based on hesitant fuzzy linguistic Z-numbers for risk prioritisation of nuclear power equipment failures

The successful diagnosis of nuclear power equipment failures plays a vital role in guaranteeing the safe operation of nuclear power systems. Failure mode and effect analysis (FMEA) is one of the most commonly used methods for identifying potential failures. However, several shortcomings associated with the conventional FMEA method limit its further application. This paper develops an extended FMEA approach based on hesitant fuzzy linguistic Z-numbers (HFLZNs). Firstly, the concept of HFLZNs is proposed to describe the evaluation information, which inherits the prominent features of the hesitant fuzzy linguistic term set and linguistic Z-numbers (LZNs). Secondly, an HFLZN assessment method is developed to determine the weights of risk factors, and the weights of experts are measured based on hesitation degree. Subsequently, considering the psychological characteristics of decision makers, Tomada de Decisão Iterativa Multicritério and LZNs are integrated to obtain the risk ranking of failure modes. Finally, the practicability of the extended FMEA method is proven by an illustrative example concerning the risk evaluation of a nuclear main pump bearing, and its robustness is verified by indepth analysis.

Least life analysis of diesel locomotive crankshaft

An analysis of the diesel locomotive crankshaft failure is presented in this paper. The crankshaft of diesel locomotives is badly affected by the peak firing pressure and cooling water temperature. The main aim of this study work was to investigate the finite element analysis on a forged steel crankshaft of diesel locomotive using ANSYS software and to suggest some novel simulation strategy that is based on the size and design consideration of the crankshaft fillet radius. The investigation included the following testing techniques and experimental process: selection of the crankshaft for investigation, visual examination, non-destructive testing of the crankshaft, failure analysis, 3D modeling of the crankshaft and finite element analysis. In this paper, a novel approach was developed in which the main crankshaft under static structural condition was analyzed by varying its fillet radius. The crankshaft was impersonated into CAM software SOLIDWORKS and after 3D modeling, its finite element analysis was performed in ANSYS software. The crankshaft was subjected to peak firing pressure and its least fatigue life was analyzed by varying the fillet radius of the crankshaft in the step of 0.5″. The analysis result shows the significant improvement in the least life cycles of the locomotive crankshaft and it was found to be 1205.9 cycles with 5/8″ fillet radius as compared to 571.54 cycles with original fillet radius size of 3/8″. The total heat flow producing thermal and surface cracks on the crankpin and main journal bearing was found to be low in the crankshaft resulting in a substantial improvement in the overall age of the locomotive crankshaft.

Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks.

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.

Techniques for Large, Slow Bearing Fault Detection

Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. This poses a critical problem for high value assets, such as drilling equipment top drives, mining equipment, wind turbine main rotors, and helicopter swash plates. An undetected bearing fault can disrupt service, and cause delays, lost productivity, or accidents. This paper examines a strategy for analysis of large slow bearings to improve the fault detection of condition monitoring systems. This helps reduce operations and maintenance cost associated with these bearing faults. This analysis is primarily concerned with vibration, and is compared to temperature and grease analysis. Data was available from three wind turbines, where one of the turbine was suspected of having a faulted main bearing.

The effects of material and structure of main bearing caps on crankshaft lubrication of diesel engine

The elastic deformation of the crankshaft and engine block has important effects on lubrication performance of main bearings of the diesel engine. But the effect of main bearing caps was rarely analyzed. The purpose of this article is to analyze and compare the tribological performances of main bearings of a 20-V medium-speed diesel engine with different materials and structures of main bearing caps. A coupled multi-elastic body and elasto-hydrodynamic model is established for a 20-V medium-speed diesel engine. The deformations of engine block, crankshaft and bearing caps are simulated with finite element modal condensation method. The bearing lubrication analysis is based on the extended Reynolds equation considering the deformation of contact surface and solved by finite difference method. Two materials and four structures of the main bearing cap are analyzed with the coupled model. The distribution of oil film pressure, thickness and bearing thermal loads are obtained and analyzed. The main bearing cap of iron is better than steel on bearing lubrication performance. With the lower edge stiffness of bearing cap, the maximum oil film pressure of bearing can be reduced by 13%. Meanwhile, the bearing thermal load can be reduced by 53%. The results show that when the bearing cap material is iron and with large trough on both sides of bearing cap, the peak value of edge pressure and the average heat load of main bearing can be reduced effectively under the premise of ensuring stiffness.

Feasibility study for the use of hydrodynamic plain bearings with balancing support characteristics as main bearing in wind turbines

The failure of a wind turbine’s main bearing leads to high maintenance costs which affects the economic competitiveness of wind energy as renewable energy source. Therefore, research is conducted to replace today’s used roller bearings by plain bearings. These can be designed segmented so that in case of a failure the segments can easily be exchanged on tower. As part of a former research project a 1 MW plain bearing with conical sliding surfaces was developed and tested on a full-scale system test bench. This contribution presents a methodology how this concept can be developed further and optimized to design a bearing in 3-point suspension or a moment bearing for high power classes.

Effect of coupling weight of marine diesel engine on crankshaft vibration and lubrication

Journal misalignment caused by coupling weight result in rotary unbalance. To study the unbalanced effect, based on AVL EXCITE PU software, coupled multi-body dynamics and lubrication model of a marine diesel engine had been built, and validated by engine experimental data. Based on the simulation model, vibration and bearing lubrication of crankshaft with different rear-end configuration had been discussed. The results show that the unbalance caused by rear end coupling weight of crankshaft have effect on the 1st and 2nd main bearing near flywheel, and have little effect on other main bearings. It also changes the pressure distribution of upper and lower shell, and brings edge thermal load increase. Meanwhile, it has more effect on the heat transfer to shell than to journal.

Research of properties of zinc-iron coating when restoring places of indigenous supports of engines of transport and technological machines

This article presents the results of experimental studies to determine the relationship between electrolysis modes and the properties of electroplated coatings, which are used in mathematical modeling of the dynamics of the electrolytic process, described by a system of ordinary differential equations due to the complex relationship between the kinetics of chemical reactions, hydrodynamics and mass transfer in the electrolyte stream, the kinematics of electrode plates, as well as the influence on all these processes of the electric field of the pair “anode-cathode”. At the same time, a series of field experiments to restore the seats of the main bearings of the cylinder blocks with galvanic coatings served as the experimental base. The final result of this study is the procedure for constructing an optimal resource-saving mode of deposition of galvanic coatings, which are a zinc-iron alloy.