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.

/pdf/remaining-useful-life-prediction-based-on-the-bayesian-4yzya4u19u.pdf

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.

/pdf/instruments-of-identification-of-hydraulic-components-1i6n3ift4q.pdf

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

Moisture is a crucial factor that significantly impacts the insulation condition of oil-paper insulation in transformers. In engineering practice, the moisture content of insulation paper is primarily obtained indirectly using moisture equilibrium curves and moisture calculation models of oil-paper insulation. The aging status of insulation paper exhibits discernible impacts on its moisture absorption characteristics. In this study, the widely adopted moisture calculation model in engineering and related research, i.e., the Fessler model, was optimized by considering the influence of the aging status. Furthermore, the optimized model was verified by comparing the calculated results with experimental data. The optimized model provides a fundamental basis for further research and practical applications in engineering.

Zhiqiang Li

Papers

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

Method to Estimate the Moisture Content of On-board Traction Transformer in EMUs