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• W4285252436 abstract "With the development of automated and integrated large-scale industrial systems, accurate and effective fault diagnosis methods are required to ensure the security and reliability of running mechanical equipment. Due to the time consumption and poor generalization performance of conventional machine learning-based methods, deep learning (DL)-based methods have wider application prospects due to their end-to-end architectural properties. However, in the DL models, problems such as a large number of trainable parameters, complicated hyperparameter tuning, and initialization instability increase the difficulty of model training and limit higher performance. To address these disadvantages of the DL method, we proposed a novel DL framework by applying convolutional neural networks (CNNs) based on the optimization of transfer learning (TL). TL can help the model achieve higher precision with less computational cost by transferring low-level features and fine-tuning high-level layers. In addition, data processing was implemented using continuous wavelet transformation (CWT) to convert vibration signals into 2-D images, and support vector machines (SVM) were employed to replace the fully connected layers for better classification. As a result, the proposed method was superior to the classical deep architecture trained from scratch. The performance of the proposed method is analyzed by presenting testing reports, convergence curves, and confusion matrixes. Moreover, experiments comprised of cross-domain diagnosis, simulated composite fault detection, and performance comparison on seven mechanical datasets, including bearings, gearboxes, and rotors, are presented. Based on these results, it can be observed that our method achieved the highest accuracy under various conditions." @default.
• W4285252436 created "2022-07-14" @default.
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• W4285252436 date "2022-01-01" @default.
• W4285252436 modified "2023-10-14" @default.
• W4285252436 title "Intelligent Machine Fault Diagnosis Using Convolutional Neural Networks and Transfer Learning" @default.
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• W4285252436 doi "https://doi.org/10.1109/access.2022.3173444" @default.
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