Abstract
Bearing state recognition, especially under variable working conditions, has the problems of low reusability of monitoring data, low state recognition accuracy and low generalization ability of the model. The feature-based transfer learning method can solve the above problems, but it needs to rely on signal processing knowledge and expert diagnosis experience to obtain the cross-characteristics of different working conditions data in advance. Therefore, this paper proposes an improved balanced distribution adaptation (BDA), named multi-core balanced distribution adaptation (MBDA). This method constructs a weighted mixed kernel function to map different working conditions data to a unified feature space. It does not need to obtain the cross-characteristics of different working conditions data in advance, which simplifies the data processing and meet end-to-end state recognition in practical applications. At the same time, MBDA adopts the A–Distance algorithm to estimate the balance factor of the distribution and the balance factor of the kernel function, which not only effectively reduces the distribution difference between different working conditions data, but also improves efficiency. Further, feature self-learning and rolling bearing state recognition are realized by the stacked autoencoder (SAE) neural network with classification function. The experimental results show that compared with other algorithms, the proposed method effectively improves the transfer learning performance and can accurately identify the bearing state under different working conditions.
Highlights
Modern industrial production technology makes great contributions to improving productivity, reducing losses, saving natural resources and human resources, reducing the scrap rate, and ensuring product quality
We propose a rolling bearing state recognition method based on a WDBA-stacked autoencoder (SAE)
This method depends on balanced distribution adaptation (BDA) theory, and constructs a weighted mixed kernel function to map different working condition data to a unified feature space, which effectively minimizes the distribution divergence between different working conditions data
Summary
Modern industrial production technology makes great contributions to improving productivity, reducing losses, saving natural resources and human resources, reducing the scrap rate, and ensuring product quality. Bearing state detection and fault diagnosis methods have used the vibration mechanism of rolling bearings and signal analysis and processing techniques to extract features, and have utilized expert diagnostic experience to achieve bearing fault diagnosis and state recognition [2,3,4,5,6,7]. These methods laid the foundation for the development of bearing diagnostics.
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