Abstract
It is very important and necessary to diagnose bearing faults timely, quickly, and accurately in practical applications, because the operation status of the bearings is directly related to the performance and reliability of the rotating machinery. Therefore, a generic intelligent bearing fault diagnosis system based on AlexNet with transfer learning was proposed to automatically identify and classify different bearing faults. Transfer learning was used to avoid overfitting problem of deep network. Five bearing faults at two different motor loads and speeds were selected from the Case Western Reserve University (CWRU) bearing dataset to validate the performance of the proposed method. Results showed that compared to previous methods, the proposed method achieved excellent performance, with overall classification accuracy over 99.7%, and fast training and testing times. Feature visualization displayed the common and high-level features of spectrograms of vibration signals learned by the trained classification model. And strongest activations demonstrated the classification model had learned the correct features of each bearing fault. Importantly, there were two testing datasets employed in this study, where the training dataset and testing dataset (2) were completely independent and the number of sub-spectrograms used for testing was 3-5 times greater than those used for training. Thus, all the results suggest that the proposed method is stable, reliable, suitable for diagnosing different bearing faults and has a great potential in practical applications.
Highlights
Rotating machinery is one of the widely used types of machines in modern industry and its reliability and safe operation are critical to the mechanical systems [1]
These results indicated that the trained classification model had learned the correct features of each bearing fault and further demonstrated that the proposed method was effective in diagnosing bearing faults
In this study, a generic intelligent bearing fault diagnosis system based on AlexNet with transfer learning was proposed to automatically identify and classify different bearing faults
Summary
Rotating machinery is one of the widely used types of machines in modern industry and its reliability and safe operation are critical to the mechanical systems [1]. The shallow structure of conventional machine learning algorithms has very limited ability to learn non-linear relation for extracting features [14] Based on these drawbacks, deep learning algorithms have been introduced in bearing fault diagnosis systems in recent years to make them more intelligent. AlexNet, one of the leading CNN architectures, is designed by Krizhevsky in 2012 [28] and has proven to achieve superior performance over previous approaches in image classification tasks [29] It is a complex and large neural network with 60 million parameters and 650,000 neurons, consisting of 5 convolutional layers, some of which are followed by max-pooling layers, and 3 fully connected layers (FCL) with final 1000-way softmax [30].
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