Abstract
To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy. First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training. Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning. The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. We have obtained advanced results on both datasets of the gearbox dataset. The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%.
Highlights
Fault diagnosis refers to the status monitoring of equipment, which has reached the prediction of its fault time and the classification of faults
The above models are based on manual feature extraction for fault diagnosis
Manual features are used for different classification tasks. is means that the features used for accurate predictions are not suitable for other scenarios in some cases
Summary
Fault diagnosis refers to the status monitoring of equipment, which has reached the prediction of its fault time and the classification of faults. Rough model training, the deep architecture can automatically choose proper representations based on the training data to help make accurate predictions in subsequent classification stages. Migrating parameters to a new model reduces training time and gets higher accuracy by using limited target data to train models Based on these advantages, transfer learning is used to solve the above problems in deep learning. With the excellent feature extraction ability of convolutional neural networks, fewer target data can be used to achieve ideal fault diagnosis accuracy. Erefore, this paper proposes a framework utilizing the transfer learning model VGG19 to reduce training time and transposed convolution feature enhancement features to reduce the sample size.
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