With the challenges posed by the intermittent nature of renewable energy, energy storage technology is the key to effectively utilize renewable energy. China’s energy storage industry has experienced rapid growth in recent years. In order to reveal how China develops the energy storage industry, this study explores the promotion of energy storage from the perspective of policy support and public acceptance. Accordingly, by tracing the evolution of the energy storage policies during 2010–2020 comprehensively, a better understanding of the policy intention and implementation can be obtained. Meanwhile, this paper collects the information of Weibo users and posts related to energy storage by web crawler technology. The status of public attention and sentiment orientation toward energy storage are investigated with a text mining method. The main results are as follows. 1) The evolution of energy storage is characterized by three stages: the foundation stage, the nurturing stage, and the commercialization stage. 2) Most people have a positive attitude towards energy storage and recognize the potential of the energy storage industry, and it is discovered that the public attitudes towards energy storage exist cognitive bias. 3) More policies concerning market mechanism, R&D, and subsidies should be introduced to enhance the effect of energy storage policies and increase public recognition. These findings help to understand the energy storage policy and provide better strategies for policymaking.

Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under different speeds and environment interference, which may lead to different diagnosis results. In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time-domain and frequency-domain features are extracted from the different speeds vibration signals. Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis.