A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu, Xiaoyang; Huang, Huimin; Xiang, Jiawei

doi:10.3390/s20020420

Cited by 52 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rolling bearing is an important component of rotating machinery, and its operation status is directly related to whether the whole equipment can run safely and smoothly. erefore, the fault analysis of rolling bearing has become a hot issue in the field of signal analysis [1][2][3]. Considering the problems of signal transmission path, signal acquisition convenience, and cost, vibration signal is a widely used analysis medium.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…ML methods can directly learn the fault discriminative features from the historical data without considering prior models [3]. Many ML algorithms have been used for REB fault diagnosis, and achieved considerable success [4][5][6][7][8][9][10][11][12]. Van et.al [13] proposed a support vector machine (SVM)-based model for REB fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset

et al. 2020

View full text Add to dashboard Cite

Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods. INDEX TERMS Rolling element bearing, fault diagnosis, signal-to-input feature mappings, deep residual networks.

show abstract

“…A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine is proposed [38]. A personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an FEM simulation driving support vector machine is proposed [39]. A new unsupervised domain adaptation method named domain-adversarial residual-transfer learning of deep neural networks is proposed to tackle cross-domain image classification tasks [40].…”

Section: Introductionmentioning

confidence: 99%

A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

It is very difficult to obtain the label data of rolling bearings under the complicated and variable working conditions, which results in low diagnosis accuracy. Transfer sparse coding(TSC) is a new feature representation method, which can effectively extract features from data matrix. Joint geometric and statistical alignment (JGSA) is a domain adaptation method, which can reduce the distribution shift and geometric shift between domains. In order to make full use of the feature extraction ability of the TSC and the transfer classification ability of the JGSA, a new transfer learing fault diagnosis(TSC-JGSA) method based on combining the characteristics of the TSC and JGSA is proposed to realize the fault diagnosis of rolling bearings under variable working conditions in this paper. In the TSC-JGSA, the fast Fourier transform technology is used to transform the time-domain signals into frequency-domain amplitudes. Then the TSC is used to effectively extract the deep features from the obtained frequency-domain amplitudes in order to construct a sparse feature matrix, which is input into the JGSA in order to realize the fault diagnosis of rolling bearings. Finally, the vibration data of rolling bearings under variable working conditions is used to prove the effectiveness of the TSC-JGSA. The experiment results show that the TSC-JGSA can effecrively solve the problem of lacking label data in actual engineering by using label data in the laboratory, and obtan higher diagnosis accuracy than other compared methods. It provides a new diagnosis idea for rotating machinery.

show abstract

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Cited by 52 publications

References 34 publications

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

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

Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset

A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

Contact Info

Product

Resources

About