Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery. In practical applications, bearings often work at various rotational speeds as well as load conditions. Yet, the bearing fault diagnosis under multiple conditions is a new subject, which needs to be further explored. Therefore, a multi-scale deep belief network (DBN) method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals, containing four primary steps: preprocessing of multi-scale data, feature extraction, feature fusion, and fault classification. The key novelties include multi-scale feature extraction using multiscale DBN algorithm, and feature fusion using attention mechanism. The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method. Furthermore, the aforementioned method is compared with four classical fault diagnosis methods reported in the literature, and the comparison results show that our proposed method has higher diagnostic accuracy and better robustness.

Unmanned Aerial Swarms are attracting widespread interest in fields such as disaster response, environmental monitoring, and agriculture. However, there is still a lack of effective recovery strategies and comprehensive performance metrics for UAV swarms facing communication attacks, especially in capturing dynamic recovery. The aim of this study is to recover the split and disconnected UAV swarm under attacks. A dynamic recovery method is proposed under attacks by establishing the relationship between algebraic connectivity and consensus speed. The proposed recovery method enables each UAV to selectively establish communication links with responsive UAVs based on the proposed recovery method to reduce communication cost, rather than linking with all neighbours within communication range. Based on this, a set of performance indexes is introduced, considering factors such as consensus ability, communication efficiency, mission execution, and resource consumption. Furthermore, a resilience metric is proposed to quantitatively assess the efficiency of recovery and consensus transition, providing a comprehensive measure of the ability to reach consensus after attacks. Simulations utilizing the second-order consensus protocol and dynamics validate that the consensus speed of the proposed recovery method is $18.88\%$ faster than random recovery. The proposed resilience metric captures the change in the time from recovery to new consensus state, and the resilience of the proposed recovery method is $66.99\%$ higher than random recovery.