Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

Zhao, Jing; Chen, Qingguang

doi:10.1155/2020/8875179

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…According to Eqs. ( 18)- (25), calculated in MATLAB software, eight time-frequency domain characteristic parameters will form a 20 × 1 matrix. This matrix is the parameter value of each group of data in each state.…”

Section: Calculation Results Of Characteristic Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Meng¹,

Wang²,

Yang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

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Section: Calculation Results Of Characteristic Parametersmentioning

confidence: 99%

“…The kurtosis K reflects the numerical statistics of the vibration signal distribution characteristics, expressed as [25,26]:…”

Section: Fault Frequency Analysis Based On Emdmentioning

confidence: 99%

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Meng¹,

Wang²,

Yang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…SVM classifies data by constructing classification hyperplane, which is mainly used to deal with the classification problems of small-and medium-sized data samples, nonlinear and high-dimensional. Zhao et al [8] used SVM to build a bearing fault diagnosis model and verified the recognition ability of SVM method to a variety of rolling bearing fault types. Bayesian algorithm calculates the probability of different events and selects the one with the greatest probability as the classification result.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Identification Method Based on ISMA-GRNN

Yang

Sun

Zhu

et al. 2022

Mathematical Problems in Engineering

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The noninvasive load monitoring method carries out load identification after event detection and feature extraction of load data. At present, nonload intrusive load monitoring faces the problems of low load identification accuracy and long load identification time. In order to solve these problems, a load identification method based on the improved slime mould algorithm-generalized regression neural network (ISMA-GRNN) is proposed. Firstly, by adding mutation operation in slime mould algorithm (SMA) position update, the global optimization ability of SMA is improved. Then, the improved slime mould algorithm (ISMA) is used to optimize the smoothing factor of GRNN and find the best smoothing factor. Finally, the best smoothing factor is input into GRNN for load identification, and the load identification results are output. To measure the effect of load identification, load identification precision, load identification accuracy, and load identification time are used as evaluation indicators. The simulation results show that compared with HHO-GRNN and WOA-GRNN, the load identification time of SMA-GRNN is greatly shortened, but the results are not satisfactory. On the basis of SMA-GRNN, ISMA-GRNN has significantly improved the accuracy and precision of load identification. In conclusion, ISMA-GRNN can better adapt to the load identification of multiple electrical equipment scenes.

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“…The main challenge becomes the optimizations of Q-factors and decomposition level when TQWT is used for fault diagnosis. Nowadays, many reasearchers have focused on the optimization of Q-factors, of which kurtosis-based method is the mostly used method to improve the TQWT [17]. Kurtosis is an effective index to characterize the impulsive feature.…”

Section: Introductionmentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Zhou²,

Gao

et al. 2021

Meas. Sci. Technol.

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In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

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Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

Cited by 15 publications

References 23 publications

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Load Identification Method Based on ISMA-GRNN

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

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