Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

Cai, Jinhua; Xiao, Yongliang

doi:10.1177/0954406218782285

Cited by 16 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, obtaining the denoised time-frequency matrix is another significant issue in the GST method. Singular value decomposition (SVD), as a non-linear filtering method, has been widely applied to reduce the signal noise in diverse practical applications (Reza and Kenan, 2015;Yu et al, 2017;Cai and Xiao, 2018;Li et al, 2016a, b). However, one of the main challenges of the SVD denoising method is selecting the effective singular value order (Zhao and Jia, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new scheme for extracting fault features of rolling element bearings

Liu

2022

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.Design/methodology/approachThe generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.FindingsThe simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.Research limitations/implicationsThis paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.Originality/valueIn the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Singular value decomposition (SVD), as a non-linear filtering method, has been widely applied to reduce the signal noise in diverse practical applications (Reza and Kenan, 2015; Yu et al. , 2017; Cai and Xiao, 2018; Li et al. , 2016a, b).…”

Section: Introductionmentioning

confidence: 99%

A new scheme for extracting fault features of rolling element bearings

Liu

2022

View full text Add to dashboard Cite

show abstract

“…Li et al [ 15 ] proposed a two-direction two-dimensional linear discriminative analysis (TD-2DLDA), which combine the advantages of the short-time Fourier transform (STFT) and wavelet transform. Cai et al [ 16 ] combined the generalized S transform and singular value decomposition (SVD) for time-frequency analysis of bearing fault vibration signals. Dhamande et al [ 17 ] employed continuous and discrete wavelet transforms of the vibration signal to extract compound fault features in gear systems.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy

Tang

Qing

et al. 2022

Entropy

View full text Add to dashboard Cite

Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the help of deep learning methods. However, traditional intelligent fault diagnosis methods still have some limitations in fault feature extraction and the latest object detection theory has not been applied in fault diagnosis. To this end, a fault diagnosis method based on a sparse short-term Fourier transform (SSTFT) and object detection theory is developed in this paper. First, a sparse constraint is introduced in time-frequency analysis to improve the time-frequency resolution of the model without cross-term interference and proximal gradient descent (PGD) is adopted to quickly and effectively optimize the model to obtain a high-quality time-frequency representation (TFR). Second, a fault diagnosis model based on a region-based convolutional neural network (RCNN) is built; the model can extract multiple regions that can characterize fault features from the TFR. This process avoids the interference of irrelevant vibration components and improves the interpretability of the fault diagnosis model. Finally, multicategory rolling bearing fault identification is realized. The effectiveness of the proposed method is validated by simulation signals and bearing experiments. The results indicate that the proposed method is more effective than existing methods.

show abstract

“…For reducing non-stationary and nonlinear signal noises, empirical mode decomposition (EMD) method (Cheng, Wang, Chen, Zhang, & Huang, 2019;Tao, 2019;Zhang, Zhao, & Deng, 2018) is a common method, but in principle there are problems with modal aliasing and endpoint effects. For noise reduction, singular value decomposition (SVD) method (An, Zeng, Yang, & An, 2017;Cai & Xiao, 2019;Wang et al, 2019) has good performance, but the number of singular values depends on the manmade. If the singular value is too large, it will influence the noise reduction effect.…”

Section: Introductionmentioning

confidence: 99%

Weak fault diagnosis of rolling bearing based on FRFT and DBN

2020

Systems Science & Control Engineering

View full text Add to dashboard Cite

When diagnosing the weak fault of rolling bearing, the fault characteristic is difficult to be extracted because the fault signal has a small amplitude and is susceptible to noise. Aiming at this problem, a fault diagnosis method is proposed based on fractional Fourier transform (FRFT) and deep belief networks (DBN). The original fault signal is first transformed into the fractional domain, and the signal is filtered in this domain to extract the fault features. The characteristic signal is then input to the DBN, and the whole network is optimized to finally realized fault diagnosis by using the pre-training and the reverse propagation algorithm. The simulation results show that the method can effectively detect the weak fault of rolling bearing.

show abstract

Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

Cited by 16 publications

References 18 publications

A new scheme for extracting fault features of rolling element bearings

A new scheme for extracting fault features of rolling element bearings

Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy

Weak fault diagnosis of rolling bearing based on FRFT and DBN

Contact Info

Product

Resources

About