Theoretical investigation of the contributions of the excitation forces to the vibration of an axial piston pump

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

A remaining useful life (RUL) prediction method for an external gear pump is proposed by Bayesian regularized radial basis function neural network (Trainbr-RBFNN). The variational mode decomposition (VMD) algorithm has been used to denoise the vibration data of accelerated degradation test, followed by which, using the Hilbert modulation the reconstructed signal has been demodulated. After which, compared with the ensemble empirical mode decomposition (EEMD) algorithm and the modified ensemble empirical mode decomposition (MEEMD) algorithm. Subsequently, factor analysis (FA) has been selected to realize the fusion of various characteristic parameters, after which, the external gear pump’s degradation evaluation index established and analyzed. Finally, the degradation evaluation index has been used to train the Trainbr-RBFNN model, and achieve gear pump degradation evaluation model for RUL prediction. Experiment results evidence that the RUL of the external gear pump can be accurately evaluated with the method used.

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Remaining Useful Life Prediction Based on the Bayesian Regularized Radial Basis Function Neural Network for an External Gear Pump

This paper presents an analysis of potential failures of hydraulic components on the example of a hydraulic gear pump with the use of instruments of quality management. The “five why” method was implemented to evaluate failures as well as the bases for the causes which have an influence on pumps failures. The causes are connected with operation condition of gear pumps and the quality of working fluid. In the next step, by using a systematic diagram, appropriate improvement actions for the identified causes were determined.

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Instruments of identification of hydraulic components potential failures

CFD-vibration coupled model for predicting cavitation in gear transmissions

A method for gear fatigue life prediction considering the internal flow field of the gear pump

An in-depth exploration of plant–soil interactions can improve our knowledge of the succession and evolution of forest ecosystems. To understand the coupling relationship between species diversity and soil physicochemical properties in natural secondary forests during the process of vegetation restoration, the species diversity of trees, shrubs and herbs and the physicochemical properties of soil at different depths were investigated in six forest communities in the Qinling Huoditang area over two years (2013 and 2019). The analysis indicated that the soil nutrient content in the region decreased during this period, but the plant diversity index showed no obvious change. Through RDA and regression analysis, we determined that the correlations between plant diversity and soil physicochemical properties were discrete. The tree and herb species diversity were most closely related to the surface soil, while shrub diversity was more strongly regulated by the middle soil layer. Available phosphorus had the strongest effect on trees, and the main factors affecting shrubs were NO3-N and NH4-N. Herb growth was more limited by soil physical properties such as the soil bulk density and porosity. We concluded that evident correlations exist between soil physicochemical properties and plant communities. After six years of natural restoration, the plant diversity index did not change significantly. However, the soil nutrient content decreased obviously. This study provides a reference for the management of vegetation restoration processes in forest ecosystems.

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Lele Qi

Papers

A method for gear fatigue life prediction considering the internal flow field of the gear pump

Coupled Relationship between Soil Physicochemical Properties and Plant Diversity in the Process of Vegetation Restoration