Fault Detection and Classification of Machinery Bearing Under Variable Operating Conditions Based on Wavelet Transform and CNN

Eltotongy, Assem; Awad, Mohammed I.; Maged, Shady A.; Onsy, Ahmed

doi:10.1109/miucc52538.2021.9447673

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The second type of statistical method is to use a dynamic or nonlinear mode for regression [10][11][12], such as the Auto-Regressive output and eXogenous input method (ARX) [10] and the principal component analysis method [11]. Other methods include building a regression model after decomposing the signals, such as wavelet transform [13][14][15]. When a new signal is reconstructed from the wavelet coefficients which correspond to the temperature effect, regression and interpolation methods may be employed to separate temperature influence.…”

Section: Introductionmentioning

confidence: 99%

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Hu,

Liu,

Deng

et al. 2024

Materials

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Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved.

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Section: Introductionmentioning

confidence: 99%

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Hu,

Liu,

Deng

et al. 2024

Materials

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show abstract

“…Zhang used PCA to reduce the data dimension from six to three, reducing the complexity of the back-end algorithm [17]. Using digital signal processing, feature extraction transforms the original time domain into a frequency domain or time-frequency domain signals, while fast Fourier transform (FFT) and wavelet transform (WT) [18][19][20][21][22][23][24] have been verified as effective methods. However, FFT is only suitable for stationary signals, and cannot be effectively analyzed for non-stationary signals caused by different time domains.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Chen

Lin

et al. 2023

Applied Sciences

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Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

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“…Yang et al [14] combined hierarchical symbolic analysis with CNN to extract the characteristics of original signals adaptively, and realize the fault diagnosis of rotating machinery. Eltotongy et al [15] preprocessed the bearing vibration signal via continuous wavelet transform, and then used a CNN to realize the condition-based maintenance of the bearing. Chen et al [16] proposed the multiscale CNN with feature alignment method.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation

Wang

Zhan

et al. 2022

Meas. Sci. Technol.

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Since rolling bearings determine the stable operation of industrial equipment, it is necessary to carry out their fault diagnosis. To improve fault diagnosis accuracy, a fault diagnosis method based on a stacked sparse autoencoder (SSAE) combined with a softmax classifier is proposed in this paper. Firstly, SSAE is used to extract frequency-domain features of vibration signals. Then, the improved K-fold cross-validation is employed to obtain the features' pre-train set, train set, and test set. Finally, the SSAE- model is constructed via the pre-train set, while the tuned model is built via the train set. The model performance is evaluated based on Accuracy, Macro-precision, Macro-recall, and Macro-F1score. The proposed model is validated by Case Western Reserve University and XJTU-SY data with 99.15% and 100% accuracy, respectively.

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Fault Detection and Classification of Machinery Bearing Under Variable Operating Conditions Based on Wavelet Transform and CNN

Cited by 9 publications

References 10 publications

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation

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