Conventional hydraulic actuators in aircraft systems are high maintenance and more vulnerable to high temperatures and pressures. This usually leads to high operating costs and low efficiency. With the rapid development of More/All Electric technology, power-by-wire actuators are being broadly employed to improve the maintainability, reliability, and manoeuvrability of future aircraft. This paper reviews the published application and development of the airborne linear electromechanical actuator. First, the general configuration, merits, and limitations of the gear-drive electromechanical actuator and the direct-drive electromechanical actuator are analysed. Second, the development state of the electromechanical actuator testing systems is elaborated in three aspects, namely the performance testing based on room temperature, testing in a thermal vacuum environment, and iron bird. Common problems and tendencies of the testing systems are summarized. Key technologies and research challenges are revealed in terms of fault-tolerant motor, high-thrust mechanical transmission, multidisciplinary modelling, thermal management, and thermal analysis. Finally, the trend for future electromechanical actuators in More/All Electric Aircraft applications is summarized, and future research on the airborne linear electromechanical actuators is discussed.

A model of load distribution over threads of planetary roller screw mechanism (PRSM) is developed according to the relationships of deformation compatibility and force equilibrium. In order to make the applied load of PRSM uniformly distributed over threads, an improvement approach is proposed, in which the parameters of thread form of roller and nut are redesigned, and the contact conditions of roller with screw and nut are changed to compensate the axial accumulative deformation of shaft sections of screw and nut. A typical planetary roller screw mechanism is taken as example to analyze the load distribution, and the effects of installation configurations, load conditions and thread form parameters on load distribution are studied. Furthermore, the improvement approach is applied to the PRSM, and it is proved to be beneficial to reach uniform load distribution over threads.

This paper presents the experimental results of four continuous reinforced concrete slabs with three compartments under different compartment fire scenarios. The research focuses on the quantitative relationships of the compartment fire temperatures, the temperature distribution along the thickness of the slabs, the vertical and horizontal deflections, the crack patterns and failure modes of the slabs and the corners' reaction forces. The results indicate that for a continuous floor slab, the central vertical deflection of the slab in the middle compartment is considerably affected by the vertical deflections of the slab in the two edge compartments. The boundary condition, the ratio and arrangement of the top reinforcement of the continuous slab, and the fire spreading scenarios have important effects on the failure mode of the slab in different compartments. It is evident that more severe cracking happened within the slab in the middle compartment compared to the two edge compartments. For the edge compartment, the slab may fail by large deflection and the integrity failure of the slab in the middle compartment may occur. Increasing reinforcement ratio and using the continuous reinforcement are the effective methods to prevent or delay the failure of the continuous slabs with any fire spreading scenarios.

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To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.