Structural integrity of the flywheel of reactor coolant pump is important for safe operation of a nuclear power plant. A shrink-fit multi-ring flywheel is designed with a fall-off function, i.e., it will separate from...Structural integrity of the flywheel of reactor coolant pump is important for safe operation of a nuclear power plant. A shrink-fit multi-ring flywheel is designed with a fall-off function, i.e., it will separate from the shaft at a designed fall-off rotation speed, which is determined by the assembly process and the gravity. However, the two factors are ignored in the analytical method based on the Lame's equation. In this work, we conducted fall-off experiments to analyze the two factors and used the experimental data to verify the validity of the analytical method and the finite element method(FEM). The results show that FEM performs better than the analytical method in designing the falloff function of the flywheel, though FEM cannot successfully predict the strain variation with the rotational speed.展开更多
Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model ...Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.展开更多
基金supported by the National Natural Science Foundation of China(No.51576125)
文摘Structural integrity of the flywheel of reactor coolant pump is important for safe operation of a nuclear power plant. A shrink-fit multi-ring flywheel is designed with a fall-off function, i.e., it will separate from the shaft at a designed fall-off rotation speed, which is determined by the assembly process and the gravity. However, the two factors are ignored in the analytical method based on the Lame's equation. In this work, we conducted fall-off experiments to analyze the two factors and used the experimental data to verify the validity of the analytical method and the finite element method(FEM). The results show that FEM performs better than the analytical method in designing the falloff function of the flywheel, though FEM cannot successfully predict the strain variation with the rotational speed.
基金supported by the National Natural Science Foundation of China (Nos.50875161 and 50821003)the Natural Science Foundation of Jiangxi Province,China (No.0450017)
文摘Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.
