Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can ea...Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can easily be separated,causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively.This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set.The frequency domain feature parameter set includes two characteristic parameters:mean and variance.After adaptively dividing the frequency band by the scale space method,the mean and variance of each band are calculated.Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis.An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode.The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes.The empirical mode with the largest margin factor is selected to envelope spectrum analysis.Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.展开更多
This paper proposes a novel nondestructive diagnostic method for flip chips based on an improved semi-supervised deep extreme learning machine(ISDELM)and vibration signals.First,an ultrasonic transducer is used to gen...This paper proposes a novel nondestructive diagnostic method for flip chips based on an improved semi-supervised deep extreme learning machine(ISDELM)and vibration signals.First,an ultrasonic transducer is used to generate and focus ultrasounds on the surface of the flip chip to excite it,and a laser scanning vibrometer is applied to acquire the chip’s vibration signals.Then,an extreme learning machine-autoencoder(ELM-AE)structure is adopted to extract features from the original vibration signals layer by layer.Finally,the study proposes integrating the ELM with sparsity neighboring reconstruction to diagnose defects based on unlabeled and labeled data.The ISDELM algorithm is applied to experimental vibration data of flip chips and compared with several other algorithms,such as semi-supervised ELM(SS-ELM),deep ELM,stacked autoencoder,convolutional neural network,and ordinary SDELM.The results show that the proposed method is superior to the several currently available algorithms in terms of accuracy and stability.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.51705203,51775243)the Natural Science Foundation of Jiangsu Province(Grant No.BK20160183)+2 种基金the Open Foundation of State Key Lab of Digital Manufacturing Equipment Technology(Grant No.DMETKF2018022)the Key Project of Industry Foresight and Common Key Technologies of Jiangsu Province(Grant No.BE2017002)and the 111 Project(Grant No.B18027).
文摘Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can easily be separated,causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively.This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set.The frequency domain feature parameter set includes two characteristic parameters:mean and variance.After adaptively dividing the frequency band by the scale space method,the mean and variance of each band are calculated.Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis.An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode.The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes.The empirical mode with the largest margin factor is selected to envelope spectrum analysis.Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.
基金supported by the fellowship of China Postdoctoral Science Foundation(Grant No.2021T140279)the National Natural Science Foundation of China(Grant Nos.51705203,51775243 and 11902124)“111”Project(Grant No.B18027)。
文摘This paper proposes a novel nondestructive diagnostic method for flip chips based on an improved semi-supervised deep extreme learning machine(ISDELM)and vibration signals.First,an ultrasonic transducer is used to generate and focus ultrasounds on the surface of the flip chip to excite it,and a laser scanning vibrometer is applied to acquire the chip’s vibration signals.Then,an extreme learning machine-autoencoder(ELM-AE)structure is adopted to extract features from the original vibration signals layer by layer.Finally,the study proposes integrating the ELM with sparsity neighboring reconstruction to diagnose defects based on unlabeled and labeled data.The ISDELM algorithm is applied to experimental vibration data of flip chips and compared with several other algorithms,such as semi-supervised ELM(SS-ELM),deep ELM,stacked autoencoder,convolutional neural network,and ordinary SDELM.The results show that the proposed method is superior to the several currently available algorithms in terms of accuracy and stability.
