Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis

Liu, Dong

doi:10.1088/1742-6596/2459/1/012081

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The AFMDPSFE algorithm operates on the optimal component: initially, the proposed compound kurtosis criterion is employed to ascertain the optimal delay time τ and embedding dimension M, and the initial ranges of the two parameters are set as 2 ⩽ M ′ ⩽ 30, τ ∈ 2, round fs fg . According to equation (20), the optimal delay time can be searched primarily, and then the optimal embedding dimension searched by combining the optimal delay time with equation (21). The relationship between the two parameters and the kurtosis value obtained at last is shown as figure 7, which can be seen as…”

Section: Simulation Modelmentioning

confidence: 99%

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An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Xin,

Jiang,

Jiang

et al. 2024

Meas. Sci. Technol.

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The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

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Section: Simulation Modelmentioning

confidence: 99%

“…There are many classic methods used to preprocess signals, such as morphological filtering [14,15], spectral kurtosis [16], and sparse representation [17][18][19]. There are also more traditional time-frequency analysis [20,21], such as short-time fourier transform, wavelet transform [22] and variational mode decomposition (VMD) [23,24].…”

Section: Introductionmentioning

confidence: 99%

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Xin,

Jiang,

Jiang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The traditional fault diagnosis methods of rolling bearings mainly consist of feature extraction and fault identification. In feature extraction, time-domain features are usually extracted by using waveform factors, peaks, etc [6,7], frequencydomain features are usually extracted by using Fourier transforms, spectral cliff, etc [8,9], and time-frequency-domain features are usually extracted by using wavelet transform, empirical modal decomposition, etc [10][11][12][13], then the obtained data are input into machine learning algorithms for fault identification [14,15]. Liu et al [16] established a new spalling propagation assessment algorithm relying on spectral amplitude ratio and statistical features, and effectively diagnosed the location and degree of spalling damage.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis model based on DSCB-NFAM

Zhao,

Guo

2023

Meas. Sci. Technol.

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Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on depth-separable convolutional block (DSCB)-non-local feature awareness module (NFAM) is proposed. Firstly, a DSCB is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a NFAM is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

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Fault Diagnosis Method for Bearing Based on Attention Mechanism and Multi-Scale Convolutional Neural Network

Shen,

Zhang

2024

IEEE Access

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Convolutional neural networks (CNNs) serve as powerful feature extraction tools capable of effectively extracting information from complex environments, thus improving the accuracy of fault identification for bearing data. In this paper, we present a method for diagnosing bearing faults using an attention mechanism and a multi-scale convolutional neural network (MSCNN). Firstly, truncate and sample the rolling bearing data, and use continuous wavelet transform to generate corresponding time-frequency images, which will be used as inputs to the neural network. Next, the MSCNN, which includes efficient convolutional modules with residual structures, is utilized to extract features from the input data while maximizing the retention of valuable information. The extracted data then undergoes feature selection through the employment of an Efficient Convolutional Module (ECM) with channel attention. Finally, after being mapped through fully connected layers, the features are fed into a softmax layer for fault category prediction. In this study, the model results were tested and verified using the Case Western Reserve University (CWRU) dataset and the bearing dataset of Jiangnan University(JNU). A comparison was made with the LeNet model, ResNet model, LSTM model, and WDCNN model. The results showed that the classification accuracy of the ten types of bearing signals at the same speed can reach 100%, and the classification accuracy of the thirty types of bearing signals at different speeds can reach over 99.4%, significantly higher than the other models. The proposed method achieves the recognition of different fault states of rolling bearings under complex conditions, including multiple operating conditions and variable operating conditions. It is capable of extracting the global characteristic information of bearing faults, resulting in high diagnostic accuracy and good generalization ability. This method can provide reference for the diagnosis of rolling bearing faults under corresponding operating conditions.

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Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis

Cited by 3 publications

References 5 publications

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Rolling bearing fault diagnosis model based on DSCB-NFAM

Fault Diagnosis Method for Bearing Based on Attention Mechanism and Multi-Scale Convolutional Neural Network

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