The multi-channel data of UAV is coupled and limited. Traditional diagnostic methods relying on precise mathematical models and expert knowledge are challenging to complete the fault diagnosis of UAV with limited data. In addition, noise and distortion in multi-channel data can lead to the degradation of data quality and has an influence on the fault diagnosis method based on deep learning. To overcome these deficiencies, an adaptive siamese network is constructed by two paralleled feature extractors with shared weights. Compared with other networks, the proposed network takes advantage of channel attention mechanism to weigh the features of different channels adaptively. Therefore, the proposed network can adaptively enhance feature weights based on channel importance and reduce the coupling effect of multi-channel data. In addition, the utilization rate of sample data can be improved by random and repeated extraction, and the problem of limited data can also be effectively solved. Finally, the proposed siamese network is applied to fault diagnosis of UAV with limited data, and the effects of the channel number, training samples, and noise on diagnosis are discussed. The experiment results show that the proposed siamese network has a high accuracy, low data dependency, and strong anti-noise. The obtained research results provide an effective method for solving the coupled multi-channel data with limited data in the fault diagnosis of UAV.

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment.

HTTP adaptive streaming with chunked transfer encoding can provide low latency live video streaming experience. However, providing high and smooth quality video streaming experience under low latency conditions poses a great challenge for Adaptive BitRate (ABR) algorithm design due to the shorter time to react to bandwidth fluctuations. To address the problem, we design a Fuzzy logic controller based ABR for low latency live video streaming with Chunked Transfer Encoding (FCTE). We take player buffer size, throughput mean, and throughput standard deviation as inputs to make bitrate decisions. By making full use of network information and dealing with uncertainties using fuzzy language, FCTE can make quick and robust bitrate decisions in the complicated network environment. We evaluate FCTE over five network scenarios released by Twitch. FCTE achieves average QoE improvement by 129%, 338%, and 127% compared with three representative algorithms: L2A, LoL, and Stallion, respectively.

With the rapid advancement of industrial technology and the continuous expansion of production scale, electromechanical special equipment (ESE), such as cranes, has become increasingly essential in industrial operations. The demands for safety, reliability, and operational efficiency of this equipment have grown significantly. Extensive research has been conducted on fault diagnosis (FD) technologies for ESE to enhance their operational lifespan and ensure personnel safety. However, despite the considerable body of research, a comprehensive and systematic review in this field remains conspicuously absent. Therefore, this paper summarizes and analyzes the FD technologies associated with ESE, detailing the principal processes and the current status of technology applications. The typical failure modes that can compromise the performance are analyzed by thoroughly examining the core components of such equipment. Moreover, an in-depth discussion on FD methodologies is provided from the perspectives of traditional methods, signal processing methods, and machine learning methods. By examining these methods side by side, their strengths, limitations, and suitability for different types of ESE are highlighted. Finally, this paper offers a forward-looking assessment of the future development trajectory for FD technologies for ESE. This paper provides a comprehensive review and outlines a future development path on FD techniques in ESE, which will contribute to the continued development of these techniques.

At present, the majority of the research on dynamic model of rolling bearings with multiple faults is conducted on the assumption that the displacement deviation between raceway and rolling element changes instantaneously. However, the deviation should vary gradually. As a result, an asymptotic model of rolling bearings with multiple faults on outer raceway is established, considering the interaction of multiple faults. Synchroextracting transformation method is employed to describe the joint time-frequency distribution of the faulty rolling bearings. The discrepancies between the asymptotic model and the traditional model are contrasted and analyzed via simulation modeling. Simultaneously, the effects of fault number and fault interval on the joint time-frequency distribution are investigated. Finally, experiments are performed to verify the rationality of the established model. Simulation results demonstrate that the joint time-frequency distribution derived by the asymptotic model includes not only the fault characteristic frequency but also the rotating frequency. The central frequency increases in a multiple of the fault characteristic frequency as the fault number increase, as does its energy amplitude. Only when multiple faults are uniformly distributed among the interval range of rolling elements, the energy amplitude of the central frequency can reach the maximum. The energy amplitude of the rotating frequency scarcely changes as the fault interval and fault number vary.

In the previous models of rolling bearings with a single fault, the displacement deviation caused by the collision of the fault to the rolling element changes instantly. However, the displacement deviation should change gradually. Here, the asymptotic idea is introduced to describe the change of the displacement deviation. The calculation method of the deviation is given. An asymptotic model of rolling bearings with an inner raceway fault is constructed. Then, the simulation of the SKF6205 bearing with a single fault is carried out. The differences between the previous model and the asymptotic model for the responses and the displacement deviation are compared. The effects of the speed and fault size on the dynamic characteristics are analyzed. Finally, the experiments are carried out to corroborate the rationality of the constructed model. The research results can provide theoretical support for the dynamic analysis, fault diagnosis, and reliability analysis of rolling bearings.