Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform

Ma, Ping; Zhang, Hongli; Fan, Wenhui; Wang, Cong

doi:10.1016/j.measurement.2019.01.036

Cited by 59 publications

(31 citation statements)

References 35 publications

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“…But when the redundancy is too small (i.e., close to 1), the frequency domain response bandwidth of the signal is narrower, resulting in local degradation of the time domain response. In this paper, 3 is selected as the redundancy of TQWT since it has been widely used in previous studies [28][29][30][31]. The kurtosis of the vibration signal is used to determine Q-factor and the optimal decomposition level ( ) of TQWT.…”

Section: Signal Denoising By Tqwtmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

Huang

2020

Int. J. Onl. Eng.

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Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

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Section: Signal Denoising By Tqwtmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

Huang

2020

Int. J. Onl. Eng.

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“…Zhao et al [19] used the particle swarm optimization algorithm to optimize TQWT parameters, combined with the spectral kurtosis index to select the optimal subband. Ma et al [20] also used particle swarm optimization to optimize the parameters of TQWT and further combined with frequency slice wavelet transform (FSWT) to select the frequency band. Ding et al [21] constructed a filter bank with almost constant bandwidth by adjusting the Q-factor and decomposition level of TQWT.…”

Section: Introductionmentioning

confidence: 99%

A 1.5D Spectral Kurtosis‐Guided TQWT Method and Its Application in Bearing Fault Detection

Zhang

Wan

et al. 2021

Shock and Vibration

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Bearings are the key parts of rotating machinery, and their operation status is related to the operation safety of the whole equipment. Vibration signals often contain periodic impulse components which can reflect the fault state of bearings. However, due to the interference of signal transmission path and the influence of operating environment noise, the periodic impulse components in the signal are often submerged by the nonperiodic transient impulse components, modulation harmonic components, and noise components. Therefore, the core problem of bearing fault diagnosis theory is used to accurately extract the frequency band of bearing fault state information and suppress the frequency band of interference information. In this paper, the signal is processed by the tunable Q-factor wavelet transform (TQWT), the midfrequency band of the signal is tightly divided by selecting different Q-values, and the 1.5D spectral kurtosis defined in frequency domain is used to select the optimal subband. Simulated analysis shows that this method can avoid low-frequency harmonic interference, nonperiodic transient impulse components, and strong noise components in the time domain. Therefore, it can effectively realize the selection of the subbands of periodic impulse components and effectively extract fault feature information. Through experimental signal analysis, TQWT has good sparsity decomposition characteristics and can reasonably divide the signal frequency band, so as to separate the useful fault characteristic frequency band and interference frequency band. At the same time, compared with the kurtosis index, 1.5D spectral kurtosis has better robustness and resolution for low signal-to-noise ratio signals, which can achieve the purpose of fault characteristic band extraction.

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“…Short-time Fourier transform (STFT) method (Khodja, Aimer, Boudinar, Benouzza, & Bendiabdellah, 2019;Li, Zhang, Qin, & Sun, 2018) is limited by the window function and has no universality. Wavelet transform method (Guo & Xiao, 2017; CONTACT Jie Ma mjbeijing@163.com Ma, Zhang, Fan, & Wang, 2019;Xu, Tian, Zhang, & Ma, 2019) needs to select an appropriate wavelet base for the fault diagnosis of rolling bearing, and the parameters are pre-set by prior knowledge, where the quality of the parameter selection often has a great impact on the results. The Mahalanobis distance is based on the distribution of features throughout the space as a basis for discrimination, but the sample classification effect is not good under strong noise (Ji, Huang, & Zhou, 2019).…”

Section: Introductionmentioning

confidence: 99%

Weak fault diagnosis of rolling bearing based on FRFT and DBN

2020

Systems Science & Control Engineering

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When diagnosing the weak fault of rolling bearing, the fault characteristic is difficult to be extracted because the fault signal has a small amplitude and is susceptible to noise. Aiming at this problem, a fault diagnosis method is proposed based on fractional Fourier transform (FRFT) and deep belief networks (DBN). The original fault signal is first transformed into the fractional domain, and the signal is filtered in this domain to extract the fault features. The characteristic signal is then input to the DBN, and the whole network is optimized to finally realized fault diagnosis by using the pre-training and the reverse propagation algorithm. The simulation results show that the method can effectively detect the weak fault of rolling bearing.

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Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform

Cited by 59 publications

References 35 publications

Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

A 1.5D Spectral Kurtosis‐Guided TQWT Method and Its Application in Bearing Fault Detection

Weak fault diagnosis of rolling bearing based on FRFT and DBN

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