Differential spectral amplitude modulation and its applications in rolling bearing fault diagnosis

Jiang, Zuhua; Zhang, Kun; Xiang, Ling; Xu, Yonggang

doi:10.1016/j.measurement.2022.111755

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…The spectral amplitude modulation (SAM) method is proposed by applying the magnitude order (MO) to the amplitude spectrum of the original signal, and the feature extraction ability can be enhanced. 28,29 To amplify the frequency components contained in signals and attenuate the uncorrelated parts, the autocorrelated envelopes and cross-correlation spectrum are employed, 30,31 which show great advantages in noise suppression. Moreover, based on the formation of ES and SES, the generalized envelope spectra (GES) and the product envelope spectrum (PES) are constructed, which is demonstrated in the fault diagnosis of REBs.…”

Section: Introductionmentioning

confidence: 99%

“…However, when the signal is masked by strong background noise, the ES and SES may not achieve good performance. The spectral amplitude modulation (SAM) method is proposed by applying the magnitude order (MO) to the amplitude spectrum of the original signal, and the feature extraction ability can be enhanced 28,29 . To amplify the frequency components contained in signals and attenuate the uncorrelated parts, the autocorrelated envelopes and cross‐correlation spectrum are employed, 30,31 which show great advantages in noise suppression.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Zhu,

Liu,

Zhu

2024

Adaptive Control & Signal

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SummarySince bearing defects usually occur which threaten the stable operation of the machinery, bearing fault detection is of great importance. However, the bearing fault signals inevitably exhibit strong interference components due to the complex structures of the real equipment, which leads to difficulty in fault feature detection. To address the problem, a fault diagnosis method based on the ensemble average of enhanced generalized envelope spectrum (EAEGES) was constructed. First, to suppress the irrelevant components, the optimal analysis frequency band was determined based on the variable frequency band division criteria. Second, the optimal signal was divided into numerous sub‐signals, the EAEGES was established based on the principles of the generalized envelope spectrum to strengthen the capability in detecting bearing fault features, while the improved pulse extraction operator was utilized as the target. Finally, abundant fault information can be distinguished in the feature map acquired using the proposed technique. This technique shows high effectiveness in extracting the defect signatures of the rolling element bearing, which is demonstrated using the simulation and experimental signals and can be applied to real applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Zhu,

Liu,

Zhu

2024

Adaptive Control & Signal

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“…Such complex operating and environmental conditions result in higher failure rates when compared with other components, thereby increasing operational and maintenance costs. Consequently, it is essential to develop an effective method for bearing fault diagnosis in order to ensure the smooth operation of rolling bearings, particularly when maintaining large-scale equipment such as wind turbines [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Application of C-InGAN Model in Interpretable Feature of Bearing Fault Diagnosis

Yang,

Liang,

Tan

et al. 2024

Entropy

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Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the interpretable feature fault diagnosis of bearings. Initially, the vibration signal is denoised and transformed into a frequency domain signal. The model consists of the two primary networks, each employing a convolutional layer and an attention module, generator (G) and discriminator (D), respectively. Latent code was incorporated into G to constrain the generated samples, and a discriminant layer was added to D to identify the interpretable features. During training, the two networks were alternately trained, and the feature mapping relationship of the pre-normalized encoder was learned by maximizing the information from the latent code and the discriminative result. The encoding that represents specific features in the vibration signal was extracted from the random noise. Ultimately, after completing adversarial learning, G is capable of generating a simulated signal of the specified feature, and D can assess the interpretable features in the vibration signal. The effectiveness of the model is validated through three typical experimental cases. This method effectively separates the discrete and continuous feature coding in the signal.

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“…owing to their harsh and complex working environment, which can easily damage other parts of the equipment and even lead to serious accidents when bearings are damaged [3,4]. Hence, an increasing number of methods have been proposed for detecting bearing faults in mechanical equipment [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An adaptive time–frequency demodulation method and its applications in rolling bearing fault diagnosis

Yang,

Zhang,

Jiang

et al. 2023

Meas. Sci. Technol.

Self Cite

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Rolling bearings are critical and easily damaged components of mechanical equipment. In practical engineering applications, the collected signals usually contain a large amount of noise, which makes fault diagnosis difficult. Based on this, this paper proposes an adaptive time-frequency demodulation method for rolling bearing diagnosis. The proposed method first obtains the complex envelope of the vibration signal in the time-frequency domain using the S transform (ST), and the time-frequency coefficient of ST can be used as the complex envelope, which is proved in detail in this paper. Subsequently, the complex envelope of the optimal slice frequency is obtained by frequency slicing to significantly weaken the interference of irrelevant noise and highlight the fault characteristics. An indicator is proposed to adaptively select an optimal slice frequency component that contains the most fault information. Finally, the slice envelope spectrum of the optimal slice frequency is obtained using Fourier transform for fault diagnosis. The feasibility of the proposed method is verified using the simulated signal. The application results of the bearing inner and outer ring fault experimental signals indicate that the proposed method is more accurate and effective for bearing fault diagnosis. Comparisons with other commonly used methods also verified the superiority of the proposed method.

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Differential spectral amplitude modulation and its applications in rolling bearing fault diagnosis

Cited by 12 publications

References 30 publications

Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Application of C-InGAN Model in Interpretable Feature of Bearing Fault Diagnosis

An adaptive time–frequency demodulation method and its applications in rolling bearing fault diagnosis

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