Generalized Synchroextracting-Based Stepwise Demodulation Transform and Its Application to Fault Diagnosis of Rotating Machinery

Lv, Yong; Wu, Hongan; Yuan, Rui; Dang, Zhang; Song, Gangbing

doi:10.1109/jsen.2023.3237323

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…To enhance the time-frequency resolution, various PPTFA techniques have been investigated and widely utilized in the fault diagnosis of rotating machinery under time-varying speeds [135], [136], [137]. They enhance the quality of TFRs via suppressing the interference terms and improving the time-frequency energy concentration.…”

Section: Bpostprocessing 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: Bpostprocessing 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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“…In this work, we exclusively focus on frequency modulation (FM) models and do not address amplitude modulation signals. The mathematical expression of a FM signal with multi-component [43] is shown in the following equation:…”

Section: Signal Modelmentioning

confidence: 99%

“…In this work, we exclusively focus on frequency modulation (FM) models and do not address amplitude modulation signals. The mathematical expression of a FM signal with multi‐component [43] is shown in the following equation:

\begin{align*}\hfill s(t)=\sum\limits _{m=1}^{M}{a}_{m}{e}^{j2\pi \int \nolimits_{0}^{t}{\text{IF}}_{m}(t)dt}+n(t)\\ \hfill t=1,\text{\ldots },T\end{align*}

where a m and IF m ( t ) respectively denote the amplitude and the IF of the m − th component for m = 1, …, M . n ( t ) is additive white noise.…”

Section: Signal Model and Time‐frequency Analysismentioning

confidence: 99%

WVD‐GAN: A Wigner‐Ville distribution enhancement method based on generative adversarial network

Quan,

Ren,

Wang

et al. 2024

IET Radar Sonar & Navi

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Time‐frequency analysis based on Wigner‐Ville distribution (WVD) plays a significant role in analysing non‐stationary signals, but it is susceptible to interference from cross‐terms (CTs) for multi‐component signals. To address this issue, a novel WVD enhancement method based on generative adversarial networks (namely WVD‐GAN) is proposed, to achieve highly‐concentrated time‐frequency (TF) representation. Specifically, a deep feature extraction module is designed with multiple residual connections in the generator of WVD‐GAN to leverage the latent information encoded in the shallow representations. Meanwhile, a simple and effective attention module is introduced to enhance auto‐term features. Moreover, a multi‐scale discriminator is proposed based on dilated convolutions to guide the generator to reconstruct high‐resolution TF images by discriminating CT. Finally, a comparative analysis is provided to demonstrate the effectiveness and robustness of the proposed method on different simulated and real‐life datasets. Extensive experiments demonstrate that the proposed method outperforms several state‐of‐the‐art methods.

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“…In the field of bearing fault diagnosis, monitoring data can provide a significant amount of information; however, bearings rarely experience failures during their extended degradation processes [7,8]. Consequently, most monitoring data primarily consists of healthy states, resulting in a lack of failure data.…”

Section: Introductionmentioning

confidence: 99%

A lightweight GAN-based fault diagnosis method based on knowledge distillation and deep transfer learning

Zhong,

Yu,

Trinh

et al. 2023

Meas. Sci. Technol.

Self Cite

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Generative adversarial networks (GANs) have shown promise in the field of small sample fault diagnosis. However, it is worth noting that generating synthetic data using GANs is time-consuming, and synthetic data cannot fully replace real data. To expedite the GAN-based fault diagnostics process, this paper proposes a hybrid lightweight method for compressing GAN parameters. First, three modules are constructed: a teacher generator, a teacher discriminator, and a student generator, based on the knowledge distillation GAN (KD-GAN) approach. The distillation operation is applied to both teacher generator and student generator, while adversarial training is conducted for the teacher generator and the teacher discriminator. Furthermore, a joint loss function is proposed to update the parameters of the student generator by combining distillation loss and adversarial loss. Additionally, the proposed KD-GAN method is combined with deep transfer learning (DTL) and leverages real data to enhance the diagnostic model’s performance. Two numerical experiments are performed to demonstrate that the proposed KD-GAN-DTL method outperforms other GAN-based fault diagnosis methods in terms of computational time and diagnostic accuracy.

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Generalized Synchroextracting-Based Stepwise Demodulation Transform and Its Application to Fault Diagnosis of Rotating Machinery

Cited by 9 publications

References 46 publications

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

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

WVD‐GAN: A Wigner‐Ville distribution enhancement method based on generative adversarial network

A lightweight GAN-based fault diagnosis method based on knowledge distillation and deep transfer learning

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