Abstract
Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.
Highlights
With the development and progress of science and technology, machinery has become more automated and intelligent
One-hot encoding technology is used to label the sample data, and the samples with the same label are randomly divided into three datasets such as the training set, the validation set, and the test set according to the predefined proportions
Intelligent fault diagnosis method based on deep learning is a hot topic of current research within the field of fault diagnosis
Summary
With the development and progress of science and technology, machinery has become more automated and intelligent. At the same time, its working environment is getting worse and worse. It requires continuous high-load work in a harsh environment with humid and corrosive gas. Rolling bearings are often used in rotating machinery, and their failure seriously affects the normal operation of rotating machinery. Periodic inspection and after-the-fact diagnosis are often used for traditional fault diagnosis of rotating machinery, but they are hard to carry out and timeconsuming for most rotating machinery operating environments. As the important part of the rotating machinery, once the failures of rolling bearings are not dealt with in time, they will directly lead to the halt of machinery and affect the normal operation of the system, which will cause significant economic losses and threaten the personal safety of workers
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