A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

Li, Ke; Su, Lei; Wu, Jingjing; Wang, Huaqing; Chen, Peng

doi:10.3390/app7101004

Cited by 66 publications

(54 citation statements)

References 23 publications

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“…The gearbox is the key unit of modern machinery and has been widely used in all kinds of engineering situations. Due to the complex structure and tough running environments of large-scale machinery, key gearbox components emerge easily to local defects, which brings about fatal accidents and generates major disruptions [1,2]. Therefore, timely detection of faults residing in rolling bearings and gears are valuable for ensuring the safe and stable operation of mechanical equipment [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Simulation Research on the Time-Varying Meshing Stiffness and Vibration Response of Micro-Cracks in Gears under Variable Tooth Shape Parameters

Liu

Zha

et al. 2019

Applied Sciences

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The gear is one of the important parts of a rotary gearbox. Once catastrophic gear failure occurs, it will cause a great threat to production and life safety. The crack is an important failure factor causing changes in time-varying stiffness and vibration response. It is difficult to effectively identify the vibration response and meshing stiffness changes when there is a fine crack in the gear. Therefore, it is of great importance to improve the accuracy of meshing stiffness calculation and dynamic simulations under micro-cracks. Investigations of meshing stiffness and the vibration response of a gearbox is almost all about fixed gear shape parameters. However, the actual production process of gear system needs to change gear shape parameters. In this paper, the meshing stiffness and vibration response of the dynamic simulation signals of gear teeth with different crack depths at different tooth shape parameters (the pressure angle, the modulus, and the tooth number) were calculated, respectively. The influence of cracks on the vibration response was investigated by the fault detection indicators, the Root Mean Square (RMS), the kurtosis, and the crest factor. The result shows that when the pressure angle and modulus change, the vibration response changes erratically. However, when the tooth numbers change, the vibration response changes regularly. The results could be a guide for choosing gears in different shape parameters when system stability is the aim.

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Section: Introductionmentioning

confidence: 99%

Simulation Research on the Time-Varying Meshing Stiffness and Vibration Response of Micro-Cracks in Gears under Variable Tooth Shape Parameters

Liu

Zha

et al. 2019

Applied Sciences

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“…Its number of components is also less than that of EMD and EEMD, and it shows better noise robustness. VMD has been widely used in many fields, such as prediction of stock price [25], short-term load [26] and solar irradiation [27], fault diagnosis [28,29], feature extraction [30,31], and so on. In order to improve the prediction accuracy, the hybrid model combined with a single model has been widely used in the field of prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Yang

2018

Information

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Abstract:The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan'an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

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“…The more complex the time series, the bigger the ApEn value. ApEn has been considered in recent years as an effective characteristic parameter in condition monitoring and fault diagnosis [20][21][22]. Caesarendra et al [23] analyzed four nonlinear features, including ApEn, largest Lyapunov exponent (LLE), and correlation dimension (CD), to provide more superior descriptive information about slewing bearings than timedomain features.…”

Section: Introductionmentioning

confidence: 99%

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Wang

Liu

et al. 2018

Shock and Vibration

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Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

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A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

Cited by 66 publications

References 23 publications

Simulation Research on the Time-Varying Meshing Stiffness and Vibration Response of Micro-Cracks in Gears under Variable Tooth Shape Parameters

Simulation Research on the Time-Varying Meshing Stiffness and Vibration Response of Micro-Cracks in Gears under Variable Tooth Shape Parameters

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

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