Bearings fault detection using wavelet transform and generalized Gaussian density modeling

Tao, Xinmin; Ren, Chao; Wu, Yongkang; Li, Qing; Guo, Wei; Liu, Rui; He, Qiang; Zou, Junrong

doi:10.1016/j.measurement.2020.107557

Cited by 47 publications

(26 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A wavelet transform is an excellent method to analyze and process signal time-frequency characteristics; readers interested in its theoretical analysis can refer to [19]. The main idea of FLP [30] is that the previous gyro signal is multiplied by the corresponding weight to forecast the gyro signal at the current moment.…”

Section: Wavelet Transform and Forward Linear Prediction Algorithm (Wmentioning

confidence: 99%

“…In this paper, parallel processing [15,16] is employed, which implies that the noise and drift in the gyroscope signal are handled synchronously. Empirical mode decomposition (EMD) [17,18] and wavelet decomposition [19] are a popular multi-scale analysis method. However, mode mixing occurs in EMD; therefore, ensemble empirical mode decomposition (EEMD) has been put forward [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

2020

View full text Add to dashboard Cite

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

show abstract

Section: Wavelet Transform and Forward Linear Prediction Algorithm (Wmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

2020

View full text Add to dashboard Cite

show abstract

“…The Euclidean distance 1× between the sample and the mean of the sample can be calculated by Equation (12), that is, the distance between the sample and the center of the sphere.…”

Section: Dthresholdsmentioning

confidence: 99%

“…To overcome the problem, Wu [11] presented the FDD method based on transfer learning for multimode chemical processes. Xinmin Tao [12] proposed a novel bearings fault detection approach based on wavelet transform and Generalized Gaussian Density (GGD) modeling. The approach assigned a label for bearing fault detection by the classifier.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Suspension System of Maglev Trains Based on Historical Health Data

Zhou

Wang

2020

IEEE Access

View full text Add to dashboard Cite

To overcome the influence of multiple operating conditions for fault detection, this paper proposes a method to detect fault for the suspension system of maglev trains. Firstly, the complex operating condition of the maglev train is divided into some simple conditions, and the operating samples are extracted through the time window in the same simple operating condition. Secondly, the features of the extracted samples are extracted by the Fast Walsh-Hadamard transform, and the noise is removed by the median filtering. Thirdly, after adopting principal component analysis to reduce the dimensionality of the feature, the mean of the feature is calculated and applied to calculate the Euclidean distance from the features of the new data obtained by the same processing in each time. Fourthly, the new Euclidean distance obeying the Gaussian distribution is obtained through the Box-Cox transformation. Finally, the fault threshold in each simple operating condition is established through the characteristics of Gaussian distribution. Taking a certain operating condition as an example, the method is compared with the three similar methods. The results reveal that the proposed method can detect the faults timely and has a low false alarm rate. Moreover, the proposed sub-healthy data are within an acceptable range. Based on these results, the proposed method can be applied in fault detection of the maglev train, providing a certain basis for fault diagnosis. INDEX TERMS Multiple operating conditions, principal component analysis, Euclidean distance, Box-Cox transformation, fault detection ZHIQIANG LONG. received the B.S. degree in automation from the

show abstract

“…Another attempt is to represent the fault feature pattern with some well-selected basis. The typical techniques including the wavelet transform [ 17 , 18 ], the empirical wavelet transform [ 19 , 20 ] and dictionary construction [ 21 ]. Provided that the prior transform basis is seldom considered, several adaptive decomposition methods are extensively developed for fault feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Zheng

Yin

Xiong

et al. 2020

Sensors

View full text Add to dashboard Cite

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

show abstract

Bearings fault detection using wavelet transform and generalized Gaussian density modeling

Cited by 47 publications

References 45 publications

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Fault Detection for Suspension System of Maglev Trains Based on Historical Health Data

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Contact Info

Product

Resources

About