Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution

Abdelkader, Rabah; Abdelhafid, KaddourĀ

doi:10.21595/jve.2017.18762

Cited by 15 publications

(4 citation statements)

References 20 publications

(27 reference statements)

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“…The correlation coefficient is an influential indicator used to figure out the linear relationship between the raw signal x(t) and the reconstructed details obtained by the DWT. This indicator is determined by the equation below [19]:…”

Section: Statistical Study Based On Rms Correlation Coefficient Energ...mentioning

confidence: 99%

Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults

Dahmane,

Berrabah,

Defdaf

et al. 2024

IJEEI

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Induction motors in modern industry are becoming more and more functional and complex. Unfortunately, these machines are not free from damages what make their fault diagnosis the most critical aspect of system monitoring and maintenance. Vibrational signal data yields relevant information about the state of the entire system, as well as specifically about one of its components that makes its analysis quite interesting. For this effect, the current paper aims to propose an automatic diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The suggested method combines the discrete wavelet transform (DWT) with the envelope spectrum (ENV) as advanced signal processing, incorporating a machine learning algorithm based on random forest classifier. The discrete wavelet transforms (DWT), using the Haar wavelet, decomposes the vibrational signal to provide both approximations and details. Each detail is then reconstructed to avoid any missing of information. To precisely select the reconstructed detail (𝑅𝑒𝑐𝑑 𝑘 ) that provides pertinent information about bearing faults, a statistical study is conducted. This study involves calculating four indicators (Root mean square (RMS), correlation coefficient (CC), energy coefficient (EC) and peak to peak (P2P) factor) is performed for each (𝑅𝑒𝑐𝑑 𝑘 ). These indicators are compared with threshold indicators, and this criterion is met by the reconstructed details 1 and 3. The obtained reconstructed details are then subjected to the spectral envelope analysis to detect the fault frequencies, which are considered as new features entering the random forest classifier model. This combination of approaches allows better feature extraction and structuring of the dataset, leading to improved accuracy of the random forest classifier, achieving a higher classification rate of more than 99,53 %. The proposed DWT-ENV-RF method indicates well its efficiency when compared to other recent works, and the attained results are all confirmed by the experimental tests conducted in the CWRU laboratory.

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Section: Statistical Study Based On Rms Correlation Coefficient Energ...mentioning

confidence: 99%

Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults

Dahmane,

Berrabah,

Defdaf

et al. 2024

IJEEI

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“…Step 6. Returning to Step 4 for the next k [20,21]. The recorded vibration signal was decomposed during the experimental studies using for methods:…”

Section: Introductionmentioning

confidence: 99%

A hybrid ICEEMDAN and OMEDA-based vibrodiagnosis method for the bearing of rolling stock

Puzyr,

Mykhalkiv,

Plakhtii

2024

Nauk. visn. nat. hirn. univ.

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Purpose. To develop a hybrid method based on ICEEMDAN and OMEDA for high-fidelity detection of the diagnostic features of the technical condition of roller bearing. Methodology. Due to digital signal processing the search was made of the informative components on the broadband spectra, envelope spectra, squared envelope spectra. The usage of the methods of mathematical statistics for the selection of informative IMFs as a result of iterative calculate procedures. The introduction of blind deconvolution method with a proper objective function for the enhancement of impulse fault features. Findings. The use of traditional spectral methods provided an incomplete list of diagnostic features of bearing roller damage. The empirical mode decomposition (EMD) method and its minor versions provide the series of intrinsic mode functions (IMFs). On the broadband vibration spectra of the selected low-frequency IMFs, diagnostic features were not detected in a sufficient number. Further research focused on the development of a hybrid method that takes into account the demodulation procedure of the high-frequency components of those IMFs that were selected according to the complex evaluation index. The Fast Kurtogram technique determined an informative frequency band of 7.2–7.5 kHz for demodulation and construction of the squared envelope spectra, where harmonics of the roller spin frequency and some side bands with a width equal to the separator rotation frequency appeared. The use of OMEDA caused the reduction of residual noise on the squared envelope spectra and bilateral sidebands to appear around all harmonics of the roller spin frequency. Originality. For the first time, the whole list of the diagnostic features of roller faults of the axle-box bearing was identified using the developed hybrid method, which involves steps of noise reduction for enhancing a physical meaning of a proper frequency components, associated with faults. The usage of the optimal minimum entropy deconvolution adjusted method helped to eliminate noise and provide all diagnostic features of the technical condition on the squared envelope spectrum. Practical value. The results of the study help to control the quality of repair of the axle-box rolling bearings on the test rig at the workshop of the freight railway car depot.

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“…Therefore, the reliability and safety of rolling bearings are crucial for the whole equipment. The fault diagnosis problem of rolling bearings needs more extensive research [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets

Qin¹,

Shi²

2022

Applied Sciences

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As a critical component in industrial systems, timely and accurate fault diagnosis of rolling bearings is closely related to reliability and safety. Since the equipment usually operates in normal conditions with few fault samples, unbalanced data distribution problems lead to poor fault diagnosis ability. To address the above problems, a two-channel convolutional neural network (TC-CNN) model is proposed. Firstly, the frequency spectrum of the vibration signal is extracted using the Fast Fourier Transform (FFT), and the frequency spectrum is used as the input to the one-dimensional convolutional neural network (1D-CNN). Secondly, the time-frequency image of the vibration signal is extracted using generalized S-transform (GST), and the time-frequency image is used as the input to the two-dimensional convolutional neural network (2D-CNN). Then, feature extraction in the convolution and pooling layers is performed in the above two CNN channels, respectively. The feature vectors obtained from the two CNN models are stitched together in the fusion layer, and the fault classes are identified using an SVM classifier. Finally, using the rolling bearing experimental dataset of Case Western Reserve University (CWRU), the fault diagnosis effect of the proposed TC-CNN model under various data imbalance conditions is verified. In comparison with other related works, the experimental results demonstrate the better fault diagnosis results and robustness of the method.

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Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution

Cited by 15 publications

References 20 publications

Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults

Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults

A hybrid ICEEMDAN and OMEDA-based vibrodiagnosis method for the bearing of rolling stock

Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets

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