Demodulated High-Order Synchrosqueezing Transform With Application to Machine Fault Diagnosis

Tu, Xiaotong; Hu, Yue; Li, Fucai; Abbas, Saqlain; Liu, Zhen; Bao, Wenjie

doi:10.1109/tie.2018.2847640

Cited by 69 publications

(22 citation statements)

References 28 publications

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“…However, increasing the SST order will bring higher calculational cost and be easily disturbed by noise. Tu et al [19] proposed a two-step algorithm, demodulated transform and high-order synchrosqueezing method to achieve a compact TF representation. Although obtaining better performance in FM signals, this method requires more computational cost than high-order SST.…”

Section: Introductionmentioning

confidence: 99%

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

et al. 2021

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The time-frequency (TF) analysis (TFA) method is an effective tool for analyzing the time-variant features of non-stationary signals. Synchrosqueezing transform (SST) is a promising TFA method that has recently shown its usefulness in a wide range of engineering signal processing applications. On the other hand, the SST method suffers from some drawbacks, one of which is that when processing the frequency-modulated (FM) signal, the TF representation will smear heavily, which hinders its application in engineering vibration signals. In this paper, we propose a new TFA method named parameterized local maximum synchrosqueezing transform (PLMSST) to study engineering vibration signals with FM characteristics. First, the limitation of SST in signal processing is discussed. Next, we demodulate the signal by parameterizing the short-time Fourier transform (STFT) to correct the deviation of instantaneous frequency (IF) estimation. Further, we detect the local maximum of the spectrogram in the frequency direction to get the accurate IF estimate, and then obtain the energy-concentrated TF representation. Finally, we introduce the reconstruction function of this method. The performance of the proposed method is validated by both the numerical and experimental signals including vibration signals of the rolling bearing and the bridge. The results show that the proposed method is more effective in processing engineering vibration signals than other TFA methods.

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Section: Introductionmentioning

confidence: 99%

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

et al. 2021

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“…It is a new generalization of the STFT-based synchrosqueezing transform by computing more accurate estimates of the instantaneous frequencies using higher order approximations both for the amplitude and phase, which results in perfect concentration and reconstruction for a wider variety of signals. Currently, FSSTH has successfully been applied to the analysis of a transient gravitational-wave signal [31], seismic time-frequency analysis [12] and machine fault diagnosis [32], [33]. However, the determination of the number of IMFs from FSSTH does not been investigated, which is key for non-stationary signal characterization and effective feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

2020

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Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a timefrequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis. INDEX TERMS Fault diagnosis, rolling bearing, time-frequency analysis, high-order synchrosqueezing transform, detrended fluctuation analysis.

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“…The aforementioned synchrosqueezing transform (SST) is used to redistribute the TFR alongside the frequency components of interest, which acts as a post-processing technique to reassign the TF distributions [17][18][19][20]. Relying on a high-order estimation of IF, Tu et al proposed the demodulated high-order synchrosqueezing method to overcome the TF diffusion caused by the rapidly varying IF [21]. Similar to the idea of demodulation, the linear chirplet transform (LCT) can also be adopted to enhance the TFR [22].…”

Section: Introductionmentioning

confidence: 99%

Matching Linear Chirplet Strategy-Based Synchroextracting Transform and Its Application to Rotating Machinery Fault Diagnosis

2020

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Various time-frequency analysis methods have been employed for the vibration signal processing of rotating machinery under time-varying speeds. However, most methods suffer from timefrequency blurriness, particularly for signals experiencing fast changes of instantaneous frequencies. Synchroextracting Transform is a powerful post-processing tool of time-frequency analysis; its results, nevertheless, greatly depend on the original time-frequency representation. This paper proposes a matching linear chirplet based synchroextracting transform to address the problem. Chirp-rate matching strategy is firstly developed to alleviate smearing problems of time-frequency representations, where the chirp-rates adaptively match true ones of signals with the guidance of kurtosis. The matching strategy is then integrated with synchroextracting transform to further sharpen the time-frequency representation. With enhanced energy concentration level and sharpened instantaneous frequency ridges, the readability of time-frequency representation can be improved, which is also echoed by more accurate extracted instantaneous frequency ridges. Rotating machinery fault diagnosis can then be realized based on the extracted time-frequency ridges. INDEX TERMS Fault diagnosis, Instantaneous frequency estimation, Synchroextracting transform, Timefrequency analysis, Time-varying speed.

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Demodulated High-Order Synchrosqueezing Transform With Application to Machine Fault Diagnosis

Cited by 69 publications

References 28 publications

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

Matching Linear Chirplet Strategy-Based Synchroextracting Transform and Its Application to Rotating Machinery Fault Diagnosis

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