Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples

Zhang, Liangwei; Zhang, Junyan; Peng, Yeping; Lin, Jing

doi:10.3390/app12147032

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Zhanget al, [26] have suggested IBSA_Net: A network that uses small samples and transfer learning to identify tomato leaf diseases. The paper presented the provided IBSA_Net, a network for identifying tomato leaf diseases that uses small sample data and transfer learning together with the Shuffle Attention method to improve feature representation.…”

Section: Literature Surveymentioning

confidence: 99%

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Kanagaraj,

Kulandaivel,

Shajin

et al. 2024

Preprint

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The emergence of new diseases on plant leaves poses a substantial threat to global food safety and agricultural productivity. To mitigate this risk, accurate and swift detection of plant illnesses is crucial, reducing unnecessary expenses and minimizing financial losses and environmental damage. This study proposes a method called Plant Leaf Disease Detection with a Constitutive Artificial Neural Network (PLDD-CANN) to provide advancements in deep learning. The approach begins by gathering data from the Plant Village dataset and subjecting it to pre-processing techniques. This includes noise removal and image enhancement using a Variational Marginalized Particle Filter (AVMPF). Next, an Adaptive Convex Clustering (ACC) method is employed for image segmentation, followed by feature extraction using Fast Fourier and Continuous Wavelet (FFCWT) transforms. Finally, a Constitutive Artificial Neural Network (CANN) is utilized to categorize the input image to one of several categories, including healthy and various disease types like Yellow Leaf Curl Virus, Septoria Leaf Spot, Two-Spotted Spider Mite, Bacterial Spot, Target Spot, Leaf Mold, Mosaic Virus, Early Blight, and Late Blight. Then, the proposed technique is simulated using Python under several performance metrics including precision, f1-score, error rate accuracy, sensitivity, specificity and ROC. The proposed PLDD-CANN method provides 26.75%, 25.83% and 27.46% higher accuracy comparing with existing methods an enhanced CNN technique for plant leaves disease diagnosis in tomato (CNN-PLDD), A Novel Approach for Plant Leaf Disease Predictions with Recurrent Neural Network RNN Classification Method (RNN-PLDD), Detection of tomato leaf diseases for agro-based industries (FRCNN-PLDD) respectively.

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Section: Literature Surveymentioning

confidence: 99%

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Kanagaraj,

Kulandaivel,

Shajin

et al. 2024

Preprint

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“…To address these challenges, various researchers have devised methods to improve the accuracy of diagnosis by learning fault features with limited training data. Specifically, these methods can be summarized as transfer learning [17][18][19], data enhancement [20][21][22], meta learning [23][24][25], and metric learning [26][27][28][29]. Some studies on small sample fault diagnosis have been published in the field of mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies on small sample fault diagnosis have been published in the field of mechanical fault diagnosis. Zhang et al [17] applied an intra-domain transfer learning strategy to fault diagnosis. Based on transfer learning, Dong et al [19] applied the diagnostic knowledge learned from simulation data to real scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

Li,

Wu,

Luo

et al. 2024

Fractal Fract

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A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions.

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An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery

Zhang

Liu

Zhou

et al. 2022

Sensors

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Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.

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Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples

Cited by 3 publications

References 36 publications

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery

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