Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on parti...Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.展开更多
Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time.For highly integrated complicated mechanical systems,the intercoupling of structure transfer paths results in a great reducti...Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time.For highly integrated complicated mechanical systems,the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission.Therefore,using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths,but accompanied by huge scientific challenges.This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration.The gear meshing stiffness(GMS)identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description.Specifically,the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces.Then the dynamic differential equations are inverted to identify the GMS.Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads.Some interesting properties are discovered in the identified GMS spectra,and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process.The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.展开更多
自适应最稀疏时频分析(adaptive and sparsest time-frequency analysis,ASTFA)方法以分解得到的单分量个数最少为优化目标,以单分量的瞬时频率具有物理意义为约束条件,使得到的分量更加合理;结合盲源分离,提出了一种基于ASTFA的盲源分...自适应最稀疏时频分析(adaptive and sparsest time-frequency analysis,ASTFA)方法以分解得到的单分量个数最少为优化目标,以单分量的瞬时频率具有物理意义为约束条件,使得到的分量更加合理;结合盲源分离,提出了一种基于ASTFA的盲源分离方法并应用于齿轮箱复合故障诊断中。该方法首先利用ASTFA将单通道源信号进行分解,然后利用占优特征值法进行源数估计,根据源数重组观测信号,最后对观测信号进行盲源分离得到源信号的估计。实验结果表明,该方法可以有效地对齿轮箱复合故障信号进行分离进而实现齿轮箱的复合故障诊断。展开更多
基金Project(50875247) supported by the National Natural Science Foundation of ChinaProject(2007011070) supported by the Natural Science Foundation of Shanxi Province, China
文摘Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.
基金supported by the Basic Research Foundation,China(Grant No.MKF20210013).
文摘Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time.For highly integrated complicated mechanical systems,the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission.Therefore,using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths,but accompanied by huge scientific challenges.This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration.The gear meshing stiffness(GMS)identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description.Specifically,the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces.Then the dynamic differential equations are inverted to identify the GMS.Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads.Some interesting properties are discovered in the identified GMS spectra,and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process.The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.
文摘自适应最稀疏时频分析(adaptive and sparsest time-frequency analysis,ASTFA)方法以分解得到的单分量个数最少为优化目标,以单分量的瞬时频率具有物理意义为约束条件,使得到的分量更加合理;结合盲源分离,提出了一种基于ASTFA的盲源分离方法并应用于齿轮箱复合故障诊断中。该方法首先利用ASTFA将单通道源信号进行分解,然后利用占优特征值法进行源数估计,根据源数重组观测信号,最后对观测信号进行盲源分离得到源信号的估计。实验结果表明,该方法可以有效地对齿轮箱复合故障信号进行分离进而实现齿轮箱的复合故障诊断。
