Deep Transfer Learning for Bearing Fault Diagnosis using CWT Time–Frequency Images and Convolutional Neural Networks

Djaballah, Said; Meftah, Kamel; Khelil, Khaled; Sayadi, Mounir

doi:10.1007/s11668-023-01645-4

Cited by 11 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future research work, we will be committed to optimizing the feature extraction ability of the present method, in order to further improve its ability to detect the bearing state under high noise background. Model Accuracy [44] 99.51% [45] 99.20% [46] 97.32% [47] 98.54% [48] 99.59% Propose method 99.57%…”

Section: Discussionmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Bearing fault diagnosis is of great significance to the normal operation of machinery, and its performance and life span directly affect the operational efficiency and safety of the whole equipment. For existing image coding methods which detecting the bearing fault with a large number of training samples and complex neural networks to achieve the desired detection performance, the Multiscale Permutation Entropy Gray Image Coding (MPEGIC) method is proposed. In order to fully extract the feature information of the time series signal, this paper uses the Multiscale Permutation Entropy (MPE) method to construct a new image coding method by calculating the alignment information of the time series to reflect the complexity and randomness of the time series at different scales, and dividing the obtained feature matrix and mapping it to the gray-scale image domain. And it is experimentally verified by Case Western Reserve University (CWRU) bearing dataset and self-made rotor experimental platform bearing dataset. The results show that the method in this paper effectively reduces the number of training samples and the number of model parameters, and maintains a better detection performance even in a strong noise background.

show abstract

Section: Discussionmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Hechifa,

Lakehal,

Nanfak

et al. 2023

Electr Eng

View full text Add to dashboard Cite

This paper discusses the impact of the feature input vector on the performance of DGA-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest (RF), tree ensemble (TE), gradient boosted tree (GBT), and extreme gradient tree (XGB). To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the RF, TE, GBT, and XGB algorithms, respectively. Furthermore, the performance of the methods based on best input feature were evaluated and compared with other methods of literature such as Duval Triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, clustering, and key gases with gas ratio methods. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the RF, TE, GBT, and XGBoost models, respectively. These diagnostic accuracies are higher than 83.15 % of the clustering method and 82.02 % of combined technique which are the best existing methods. Even if the performance of DGA-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods.

show abstract

Enhanced lithium–ion battery state of charge estimation in electric vehicles using extended Kalman filter and deep neural network

Djaballah,

Negadi,

Boudiaf

2024

Int. J. Dynam. Control

View full text Add to dashboard Cite

Deep Transfer Learning for Bearing Fault Diagnosis using CWT Time–Frequency Images and Convolutional Neural Networks

Cited by 11 publications

References 24 publications

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Enhanced lithium–ion battery state of charge estimation in electric vehicles using extended Kalman filter and deep neural network

Contact Info

Product

Resources

About