Research on Fault Diagnosis Method Based on Improved CNN

Hao, Hu; Feng, Fuzhou; Zhu, Junzhen; Xun, Zhou; Jiang, Pengcheng; Feng, Jiang; Jun, Xue; Yazhi, Li; Guanghui, Sun

doi:10.1155/2022/9312905

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Guo et al 10 proposed a model combining CNNs and BiLSTMs to adaptively extract features for bearing fault diagnosis. Luo et al 11 introduced a fault diagnosis method combining dual-path Laplace wavelet convolution and bidirectional gated recurrent units, showing good generalization, robustness, and diagnostic efficiency in noisy environments. Ding et al 12 combined Temporal Convolutional Networks (TCN), soft threshold algorithms, and self-attention mechanisms to propose the SAM -TCN -ST neural network model for intelligent identification of rotating machinery faults, demonstrating higher fault identification rates than baseline methods.…”

Section: Introductionmentioning

confidence: 99%

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model

Liu,

Zhao,

et al. 2024

Preprint

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In response to the challenges posed by imbalanced failure diagnosis samples, limited labeled data, and significant computational costs in actual industrial production settings, this paper introduces a high-precision, low-resource, end-to-end fault diagnosis framework. On one hand, we propose a data augmentation method based on GCGAN, which combines CNN and GRU to construct core network structures for the generator and discriminator. We integrate a novel Smoothed Hinge-Cross-Entropy loss function to facilitate the training process, effectively mitigating mode collapse and vanishing gradient issues. On the other hand, we design a lightweight fault diagnosis model based on MDSCNN-ICA-BiGRU. By substituting standard convolutions with depthwise separable convolutions on deeper channels, the model complexity is significantly reduced, facilitating effective extraction of multiscale spatial features. The improved Coordinate Attention (CA) mechanism filters out noise and enhances the extraction of high-frequency characteristics. Combined with BiGRU, the model captures global temporal associations, achieving a fusion of spatiotemporal features. Experimental results demonstrate that the proposed approach performs well on both publicly available simulation datasets and private laboratory datasets. Compared to other benchmark methods, the GCGAN module significantly enhances data augmentation, improving classification accuracy on CNNs by 10%. When compared with classic convolutional networks such as DRSN and WDCNN, our MDSCNN-ICA-BiGRU shows faster and more stable convergence rates, with near-100% accuracy on test sets and an average computation cost reduction of approximately 70%. Even in noisy environments, our method maintains high accuracy with a slow rate of precision decay, indicating robustness and generalization capabilities.

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Section: Introductionmentioning

confidence: 99%

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model

Liu,

Zhao,

et al. 2024

Preprint

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“…Gu et al [13] improved the integrity and accuracy of bearing surface defect segmentation by preprocessing the bearing image with a gamma correction algorithm and improved the Canny algorithm. In previous studies, we have done vibration signal-based bearing fault diagnosis and image segmentation-based fault diagnosis of bearing cage, respectively, and both have achieved good detection performance [14,15]. Although the above studies can obtain better detection performance, there are still areas for improvement.…”

Section: Introductionmentioning

confidence: 99%

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Hao

Zhang

2023

Meas. Sci. Technol.

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During the assembly process of deep groove ball bearings, due to defective parts and unqualified assembly process, various indentations and scratches on the dust cover will often result in reducing the service life and reliability of the bearing. Therefore, the online monitoring of the assembly quality of the dust cover ensures the necessary detection process of the bearing surface quality. This paper proposed a bearing dust cover defect detection method based on machine vision and multi-feature fusion algorithm, which can effectively detect bearings with dust cover defects. The algorithm first performs Laplace transform and Sobel operator image enhancement on the collected bearing images. Extract and fuse multi-source fault feature with SIFT-BoVW and GLCM-Hu methods. Machine learning and deep learning models were constructed, and the performance of each model was compared through feature visualization and misclassified analysis. The results show that the extracted multi-source features are more representative and robust. The SIFT-BoVW-GS-SVM model achieved the best detection results in detecting bearing dust cover defects with an accuracy of 91.11%. The processing and program detection time for each bearing image is about 0.019s. The accuracy and speed of detection and judgment meet the needs of online defect detection of bearing dust cover.

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“…In recent years, with the widespread application of probability and statistical theory and the rise of big data, the machine learning model has also been applied in the field of LIB SOH prediction. Examples of these algorithms include the decision trees (DT) [13], the convolutional neural networks (CNN) [14], the long shortterm memory neural networks (LSTM) [15], and the random forest (RF) [16], etc. These algorithms form a data-driven approach for the SOH of battery assessment.…”

Section: Introductionmentioning

confidence: 99%

“…The convolutional neural networks (CNN) is a type of feedforward neural network that includes convolutional computations and has a deep structure. It is one of the representative algorithms in deep learning and possesses strong data feature extraction capabilities [14]. Wang et al [21] proposed integrating CNN and wavelet transform to make full use of the ability of CNN to extract features hierarchically, and wavelet transform to analyze frequencies, so as to predict the SOH of the battery; Din et al [22] used CNN to extract features from the collected image data of a large number of common battery manufacturing faults, reducing human intervention and saving time; Ruan et al [23] proposed a CNNbased model to explain the relationship between the charging data and the SOH, so as to diagnose the SOH of the LIB; Li et al [24] proposed using 1D-CNN technology to learn the nonlinear relationship between VRB current, flow rate, state-of-charge and voltage.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

Liang,

Wang,

Zhan

et al. 2023

Applied Sciences

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The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge–discharge cycle experimental data for LiFePO4 LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.

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Research on Fault Diagnosis Method Based on Improved CNN

Cited by 7 publications

References 25 publications

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

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