Fatigue Damage of an Asperity in Frictionless Normal Contact with a Rigid Flat

Surface fatigue wear widely exists, and it occurs as long as a sufficient number of loading–unloading cycles are applied. Slowing down surface fatigue wear requires understanding the evolution of fatigue damage in the surface. Real surfaces are composed of many asperities; therefore, it is important to study the fatigue damage of a single asperity. A finite element model of an asperity subjected to cyclic elastic–plastic normal loading was developed under frictionless contact condition. The asperity can be either completely or partially unloaded in a loading cycle. For the sake of completeness, both cases were investigated in the present study. The multiaxial Fatemi-Socie fatigue criterion was adopted to evaluate the fatigue damage of the asperity in elastic shakedown state, which was achieved after several loading cycles. For the case of complete unloading, severe fatigue damage was confined in a subsurface ridge starting from the edge of the maximum loaded contact area. The shape and volume of the wear particles were predicted based on a fundamentally valid assumption. For the case of partial unloading, the fatigue damage was much milder. Finally, potential research directions to expand the current study are suggested

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences

Residual Stress Distribution Design for Gear Surfaces Based on Genetic Algorithm Optimization

The rolling contact fatigue of gear surfaces in a heavy loader gearbox is investigated under various working conditions using the critical plane-based multiaxial Fatemi–Socie criterion. The mechanism for residual stress to increase the fatigue initiation life is that the compressive residual stress has a negative normal component on the critical plane. Based on this mechanism, the genetic algorithm is used to search the optimum residual stress distribution that can maximize the fatigue initiation life for a wide range of working conditions. The optimum residual stress distribution is more effective in increasing the fatigue initiation life when the friction coefficient is larger than its critical value, above which the fatigue initiation moves from the subsurface to the surface. Finally, the effect on the fatigue initiation life when the residual stress distribution deviates from the optimum distribution is analyzed. A sound physical explanation for this effect is provided. This yields a useful guideline to design the residual stress distribution

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Abstract Variational mode decomposition (VMD) is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters: the decomposed number K and penalty factor α under strong noise interference. To solve this issue, this study proposed self-tuning VMD (SVMD) for cavitation diagnostics in fluid machinery, with a special focus on low signal-to-noise ratio conditions. A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition. A hybrid optimized sparrow search algorithm (HOSSA) was developed for optimal α fine-tuning in a refined space based on fault-type-guided objective functions. Based on the submodes obtained using exclusive penalty factors in each iteration, the cavitation-related characteristic frequencies (CCFs) were extracted for diagnostics. The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition. The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs. Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost. SVMD especially enhances the denoising capability of the VMD-based method

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences

Residual Stress Distribution Design for Gear Surfaces Based on Genetic Algorithm Optimization

The rolling contact fatigue of gear surfaces in a heavy loader gearbox is investigated under various working conditions using the critical plane-based multiaxial Fatemi–Socie criterion. The mechanism for residual stress to increase the fatigue initiation life is that the compressive residual stress has a negative normal component on the critical plane. Based on this mechanism, the genetic algorithm is used to search the optimum residual stress distribution that can maximize the fatigue initiation life for a wide range of working conditions. The optimum residual stress distribution is more effective in increasing the fatigue initiation life when the friction coefficient is larger than its critical value, above which the fatigue initiation moves from the subsurface to the surface. Finally, the effect on the fatigue initiation life when the residual stress distribution deviates from the optimum distribution is analyzed. A sound physical explanation for this effect is provided. This yields a useful guideline to design the residual stress distribution

Modeling of the Non-Braided Fabric Composite Rubber Hose for Industrial Hose Pump Design

Due to the harsh operating conditions in an industrial hose pump, accurate numerical simulations of the hose with high speed would be significant but tough. The main goal of this paper is to develop a reliable numerical model with the acceptable complexity for a non-braided fabric composite rubber hose used in industrial hose pumps. A finite element model with rebar elements dealing with the non-braided fabric layers is established. Two practical tests for the counter force and profile deformation characteristics of the hose during compressing are designed. The simulation results show a good accuracy. Based on the feasible numerical model, further studies on dependencies of counter force, maximum strain and stress and the area size in contact with the inner surface of the hose on the pressing displacement are carried out, which will help engineers to decide the pressing displacement and cut down the time and cost of prototype testing. The predicted pressing displacements to seal off the hose under different pressures are also given. The methodology of modeling a hose pump hose proposed by this paper is helpful to the fully virtual simulation and design of a hose pump

State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles under Extreme Operating Temperatures Based on an Adaptive Temporal Convolutional Network

The accurate estimation of state of charge (SOC) under various conditions is critical to the research and application of batteries, especially at extreme temperatures. However, few studies have examined the SOC estimation performance of estimation algorithms for several types of batteries under such conditions. In this study, a new method was derived for SOC estimation and a series of experiments were conducted covering five types of lithium-ion batteries with three kinds of cathode materials (i.e., LiFePO4, Li(Ni0.5Co0.2Mn0.3)O2, and LiCoO2), three test temperatures, and four real driving cycles to verify the proposed method. The test temperatures for battery operation ranges from −20 to 60 °C. Then, an adaptive machine learning (ML) framework based on the deep temporal convolutional network (TCN) and Coulomb counting method was proposed, and the structure of the estimation model was designed through the Taguchi method. The accuracy and generalizability of the proposed method were evaluated by calculating the estimation errors and their standard deviations (SDs), its average errors showed a decline of at least 49.66%, and its SDs showed a decline of at least 45.88% when compared to four popular ML methods. These traditional ML methods performed poor accuracy and stability at extreme temperatures (−20 and 60 °C) when compared to 25 °C, while the proposed adaptive method exhibited stable and high performances at different temperatures

Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately

Modeling of the Non-Braided Fabric Composite Rubber Hose for Industrial Hose Pump Design

Due to the harsh operating conditions in an industrial hose pump, accurate numerical simulations of the hose with high speed would be significant but tough. The main goal of this paper is to develop a reliable numerical model with the acceptable complexity for a non-braided fabric composite rubber hose used in industrial hose pumps. A finite element model with rebar elements dealing with the non-braided fabric layers is established. Two practical tests for the counter force and profile deformation characteristics of the hose during compressing are designed. The simulation results show a good accuracy. Based on the feasible numerical model, further studies on dependencies of counter force, maximum strain and stress and the area size in contact with the inner surface of the hose on the pressing displacement are carried out, which will help engineers to decide the pressing displacement and cut down the time and cost of prototype testing. The predicted pressing displacements to seal off the hose under different pressures are also given. The methodology of modeling a hose pump hose proposed by this paper is helpful to the fully virtual simulation and design of a hose pump