A bearing vibration data analysis based on spectral kurtosis and ConvNet

Udmale, Sandeep S.; Patil, Sangram; Phalle, Vikas M.; Singh, Sanjay Kumar

doi:10.1007/s00500-018-3644-5

Cited by 41 publications

(23 citation statements)

References 43 publications

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“…The spectral kurtosis was another technique used to preprocess the vibration signal. Udmale et al [ 54 ] confirmed that the maximum decomposition level of the kurtogram revealed more frequency information, because the plane (f, ∆f) becomes finer with an increase in the decomposition level. However, this maximum level of decomposition is determined by the length of the signal used.…”

Section: Methodsmentioning

confidence: 99%

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Inyang

Petrunin

Jennions

2021

Sensors

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Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

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Section: Methodsmentioning

confidence: 99%

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Inyang

Petrunin

Jennions

2021

Sensors

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show abstract

“…Further experiments validated the performance of the proposed model. Udmale et al (2018) used the spectral kurtosis (SK) as the input of the CNN, and the experiments demonstrated the superior classification performance of this method even under different operating conditions. Cao et al (2019) decomposed the vibration signals with a discrete wavelet transfer, and the reconstructed results were concatenated into 2D matrices as the input of a CNN model.…”

Section: Introductionmentioning

confidence: 99%

A novel ResNet-based model structure and its applications in machine health monitoring

Duan

Shi

Zhou

et al. 2020

Journal of Vibration and Control

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Machine health monitoring has become increasingly important in modern manufacturers because of its ability to reduce downtime of the machine and cut down the production cost. Enormous signals acquired from machinery are capable of reflecting current working conditions by in-depth analysis with various data-driven methods. Hand-crafted feature extraction and representation from the traditional methods are essential but daunting tasks, and these methods may not be suitable for these massive data. Compared with traditional methods, deep learning ones are able to extract the best feature combination during model training without any artificial intervention, which makes it easier, more efficient, and more effective to monitor machine health, but the training cost and training time hamper its application. The short-time Fourier transform is adopted as the data preprocessing method to cut down the training cost and boost the training procedure. Inspired by the great achievements of ResNet, the new optimized model based on ResNet has been proposed with layer-by-layer dimension reduction of the feature maps. The proposed model is also able to avoid information loss in the conventional pooling layer. All the potential candidate model blocks are introduced and compared, and the best one is selected as the final one. Repeated model block layers are adapted for the best feature combinations, followed by a two-layer full connection layer for the final targets. The proposed method is validated by conducting experiments on bearing fault diagnosis and tool wear prediction dataset. The final results show that the proposed model achieves the best accuracy rate in the classification task and the lowest root mean squared error in the prediction task.

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“…Zhu et al [104] proposed a symmetrized dot pattern to transform vibration signals into 2-D images and the trained a convolutional network for fault diagnosis. Li et al[105] used the K-singular value decomposition to enhance the resolution of time-frequency features obtained by Wigner-Ville Distribution and then built CNN for planetary gearbox fault classification.Udmale et al[106] utilized the kurtogram of raw signals to train CNN for bearing fault diagnosis. In[107], Senanayaka proposed a gearbox fault diagnosis method based on multiple classifiers and data fusion.…”

mentioning

confidence: 99%

A comprehensive review on convolutional neural network in machine fault diagnosis

et al. 2020

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A bearing vibration data analysis based on spectral kurtosis and ConvNet

Cited by 41 publications

References 43 publications

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

A novel ResNet-based model structure and its applications in machine health monitoring

A comprehensive review on convolutional neural network in machine fault diagnosis

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