Velocity Synchronous Chirplet Extracting Transform: An Effective Tool for Fault Diagnosis of Variable-Speed Rotational Machinery

Lv, Yong; Ma, Yubo; Yuan, Rui; Lv, Site

doi:10.1109/jsen.2022.3177249

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…For example, Zhu et al [119] proposed a multisynchrosqueezing chirplet transform (MSSCT), which combined the MSST with a CT and can produce a more accurate IF estimator to correct the time-frequency energy deviation. Owing to the merits of the synchroextracting transform (SET) [126] , a few improved versions of the CT have been proposed, such as the synchroextracting chirplet transform (SECT) [120] , general synchroextracting chirplet transform (GSECT) [158] , velocity synchronous chirplet extracting transform (VSCET) [159], and the combination of the polynomial chirplet transform and synchroextracting transform [160] , synchroextracting frequency synchronous chirplet transform (SEFSCT) [161] , multiple squeezing based on velocity synchronous chirplet transform (MSVSCT) [162]. The main idea of these methods is that the time-frequency information around the IFs is retained and the smeared time-frequency energy is removed to enhance the energy concentration and readability.…”

Section: Cchirplet Transform-based Time-frequency Analysis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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Rotating machinery often works under time-varying speeds, and nonstationary conditions as well as harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods including nonstationary signal processing methods and data-driven methods have been developed. This paper presents a comprehensive review on the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation and generalized demodulation, are reviewed. Second, the time-frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction-and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed.We not only review the relevant state-of-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages as well as the challenges that will be encountered when applying them to industrial applications. This paper is expected to provide new graduate students, institutions and companies with a preliminary understanding of the methods on this topic.

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Section: Cchirplet Transform-based Time-frequency Analysis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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“…After that, the synchrosqueezing transform (SST) 11 is proposed to squeeze the time-frequency coefficients into the instantaneous frequency (IF) trajectory along the frequency axis, the method could provide fine time-frequency readability. In other words, the blurry time-frequency representation is concentrated by using a synchrosqueezing operator when analyzing a stationary signal, as a result, an accurate time-frequency representation is obtained 12 . Nevertheless, the fitted time-frequency curve is heavily biased in comparison with the real IF when analyzing chirp signals or frequency-modulated signals 13,14 .…”

Section: Refining the Time-frequency Characteristic Of Non-stationary...mentioning

confidence: 99%

Refining the time–frequency characteristic of non-stationary signal for improving time–frequency representation under variable speeds

Liu

Xiang

Jiang

et al. 2023

Sci Rep

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Time–frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time–frequency domain for accurate detection. In this article, an adaptive weighted smooth model is presented as a post-processing tool to refine the time–frequency ridge which is based on the coarse estimated time–frequency ridge using newly emerging time–frequency methods. Firstly, the coarse ridge is estimated by using multi-synchrosqueezing transform for vibration signal under variable speed conditions. Secondly, an adaptive weighted method is applied to enhance the large time–frequency energy value location of the estimated ridge. Then, the reasonable smooth regularization parameter associated with the vibration signal is constructed. Thirdly, the majorization–minimization method is developed for solving the adaptive weighted smooth model. Finally, the refined time–frequency characteristic is obtained by utilizing the stop criterion of the optimization model. Simulation and experimental signals are given to validate the performance of the proposed method by average absolute errors. Compared with other methods, the proposed method has the highest performance in refinement accuracy.

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“…Chen proposed the synchrosqueezed chirplet transforms to enhance the results of the TFR of LCT, getting the TFA results with high contrast [27]. There are also many parametric TFA methods coupled with post-processing methods, such as Local maximum synchrosqueezes form SBCT [28], High-order synchroextracting chirplet transform [29], synchrosqueezing polynomial chirplet transform [30], velocity synchronous chirplet extracting transform [31], local maximum synchrosqueezing chirplet transform [32], etc. Similar to the post-processing methods of STFT, although the TF resolution is improved to some extent, the original TFA methods are still essentially required to have the ability to identify the characteristic frequencies.…”

Section: Introductionmentioning

confidence: 99%

Adaptive scale chirplet transform and its application to bearing fault analysis

Hou

Han

Bai

et al. 2023

Meas. Sci. Technol.

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In response to the problems of biased estimation of instantaneous frequency (IF) and poor noise immunity in current time-frequency (TF) analysis methods, the Adaptive scale chirplet transform (ASCT) is proposed in this paper. The core idea of the proposed algorithm is to use a frequency-dependent quadratic polynomial kernel function to approximate the IF of the signal and to use the time-varying window length to overcome the frequency resolution problem due to the change in signal modulation. This method can dynamically select suitable parameters and overcome the disadvantage of unfocused energy of TF distribution. The experimental results show that the ASCT algorithm has high TF aggregation and can suppress noise interference well. In practical signal processing, the advantage of the ASCT algorithm is that it can accurately depict the characteristic frequency of the signal and detect the fault in the bearing signal. Both simulation and experimental results prove the strong realistic relevance of this algorithm.

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Velocity Synchronous Chirplet Extracting Transform: An Effective Tool for Fault Diagnosis of Variable-Speed Rotational Machinery

Cited by 11 publications

References 38 publications

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Refining the time–frequency characteristic of non-stationary signal for improving time–frequency representation under variable speeds

Adaptive scale chirplet transform and its application to bearing fault analysis

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