An Improved Time‐Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

Ma, Zengqiang; Ruan, Wanying; Chen, Mingyi; Li, Xiang

doi:10.1155/2018/8710190

Cited by 13 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However TFR which is the core part of DUET fail to localize and separate individual signals when STFT is used due to fixed spectral resolution caused by predetermined window width. Various potential methods like SET and SST has been used to increase the sharpness of TFR and is used in various application like identifying power quality disturbances [19], fault diagnosis in rolling bearing [20], hydrocarbon detection [21], model based deep learning [22][23], seismic time-frequency analysis [24] etc. Hence SST and SET is an ideal method for improving TF resolution in DUET which ultimately results in high quality speech separation especially in noisy conditions.…”

Section: Methodsmentioning

confidence: 99%

High clarity speech separation using synchro extracting transform

Abdulla

Jayakumari²

2021

IJECE

View full text Add to dashboard Cite

Degenerate unmixing estimation technique (DUET) is the most ideal blind source separation (BSS) method for underdetermined conditions with number of sources exceeds number of mixtures. Estimation of mixing parameters which is the most critical step in the DUET algorithm, is developed based on the characteristic feature of sparseness of speech signals in time frequency (TF) domain. Hence, DUET relies on the clarity of time frequency representation (TFR) and even the slightest interference in the TF plane will be detrimental to the unmixing performance. In conventional DUET algorithm, short time Fourier transform (STFT) is utilized for extracting the TFR of speech signals. However, STFT can provide on limited sharpness to the TFR due to its inherent conceptual limitations, which worsens under noise contamination. This paper presents the application of post-processing techniques like synchro squeezed transform (SST) and synchro extracting transform (SET) to the DUET algorithm, to improve the TF resolution. The performance enhancement is evaluated both qualitatively and quantitatively by visual inspection, Renyi entropy of TFR and objective measures of speech signals. The results show enhancement in TF resolution and high clarity signal reconstruction. The method also provides adequate robustness to noise contamination.

show abstract

Section: Methodsmentioning

confidence: 99%

High clarity speech separation using synchro extracting transform

Abdulla

Jayakumari²

2021

IJECE

View full text Add to dashboard Cite

show abstract

“…Based on the above shortcomings of the SST, some improved SST algorithms have appeared in recent years. In reference [32], the SST method based on variational mode decomposition (VMD) is proposed. The introduction of VMD enhances the noise robustness of the SST.…”

Section: A Related Research Of the Sstmentioning

confidence: 99%

“…(1) In the ( , ) u , , , af  are obtained according to (25) and (26) by a 2-D search. (3) The time-frequency energy of the FRSST for the signal is obtained by (32), and the instantaneous frequency ridge is obtained by the improved peak search in Section IV.B.…”

Section: Instantaneous Frequency Estimation Of a Multicomponent Simentioning

confidence: 99%

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

Liu

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The time-frequency energy distribution processed by a short-time Fourier transform can be compressed to the real instantaneous frequency by the synchrosqueezing transformation (SST), which improves the time-frequency energy concentration of the signal. However, there is a large error in the instantaneous frequency estimation of a multicomponent nonpure harmonic signal by the SST. Therefore, a method for determining the instantaneous frequency (IF) of a rolling bearing based on a fractional synchrosqueezing transformation (FRSST) is proposed. First, the theoretical derivation of the FRSST algorithm as a signal processing technique is given and the steps of the IF estimation are presented. Second, the main advantages of the proposed FRSST algorithm are proved. In the comparison of simulation signals, it is verified that the FRSST algorithm has a high time-frequency concentration, is non-fragile to the frequency modulation rate, has noise robustness and has nonsensitivity to the cross-frequency signal. Finally, the FRSST algorithm is applied to the IF estimation of a rolling bearing under rising speed and fluctuated speed, and is compared with the SST based on variational mode decomposition (VMD-SST), the generalized parametric SST (PSST) and polynomial chirplet transform (PCT). The test results show that the estimation error of IF based on the FRSST method is the least for a rolling bearing with the four fault types under rising speed. On average, the estimation error is 2.2180 Hz less than the corresponding error of the VMD-SST and 1.1862 Hz less than the corresponding error of the PSST method. INDEX TERMS Fractional synchrosqueezing transformation, instantaneous frequency, noise robustness, rolling bearing.

show abstract

“…For the vibration signal of rolling bearings, it usually presents multisource impact characteristics, which are typically nonlinear and nonstationary [9,10]. Because of the special mapping relationship between the excitation of equipment components and the frequency of vibration signals, the focus of mechanical fault diagnosis research is mainly on spectral analysis and timefrequency analysis [11,12]. At present, there are two frequency concepts that are well known.…”

Section: Introductionmentioning

confidence: 99%

Complexity Analysis of Time‐Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency

Tang

Feng

Zou

2019

Shock and Vibration

View full text Add to dashboard Cite

The multisource impact signal of rolling bearings often represents nonlinear and nonstationary characteristics, and quantitative description of the complexity of the signal with traditional spectrum analysis methods is difficult to be obtained. In this study, firstly, a novel concept of local frequency is defined to develop the limitation of traditional frequency. Then, an adaptive waveform decomposition method is proposed to extract the time-frequency features of nonstationary signals with multicomponents. Finally, the normalized Lempel–Ziv complexity method is applied to quantitatively measure the time-frequency features of vibration signals of rolling bearings. The results indicate that the time-frequency features extracted by the proposed method have clear physical meanings and can accurately distinguish the different fault states of rolling bearings. Furthermore, the normalized Lempel–Ziv complexity method can quantitatively measure the nonlinearity of the multisource impact signal. So, it supplies an effective basis for fault diagnosis of rolling bearings.

show abstract

An Improved Time‐Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

Cited by 13 publications

References 18 publications

High clarity speech separation using synchro extracting transform

High clarity speech separation using synchro extracting transform

Fractional Synchrosqueezing Transformation and its Application in the Estimation of the Instantaneous Frequency of a Rolling Bearing

Complexity Analysis of Time‐Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency

Contact Info

Product

Resources

About