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The copolymer microspheres of styrene (St) and maleic anhydride (MA) were synthesized by stabilizer-free dispersion polymerization, and the polymerization process was explored in detail. The results showed that the homopolymerization of St formed in initial polymerization period served as stabilizer, and reaction solvent of closer solubility parameter would benefit the stabilizer-free dispersion polymerization. In addition, some principal factors affecting the microspheres size, such as reaction time, reaction temperature, monomer concentration, molar feed ratio, reaction media, and cosolvent, were investigated as well. V C 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 48: 5652-5658, 2010

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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.