The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem to recognize the intervals of complex data curves, which fundamentally affect the sensitivity of feature extraction and the prediction of an assessment model. Aiming at the problem, a subway sliding plug door system health state adaptive assessment method is proposed based on interval intelligent recognition of rotational speed operation data curve. In the proposed method, firstly, the rotational speed operation data curve is adaptively divided by a long short-term memory (LSTM) neural network into four intervals, according to the motion characteristics of the door system. Secondly, the sensitive features of the door system are screened out by the random forest (RF) algorithm. Finally, the health state of the door system is assessed using the adaptive boosting (AdaBoost) classifier. The proposed method is comprehensively verified by the benchmark experiment data set. The results show that the average diagnostic accuracy of the method on multiple bench doors can reach 98.15%. The wider application scope and the higher state classification accuracy indicate that the proposed method has important engineering value and theoretical significance for the health management of subway sliding plug door systems. Full article

This paper deals with the control development of a wind energy conversion system (WECS) interfaced to a utility grid by using a doubly fed induction generator (DFIG), a back-to-back (B2B) converter and an RL filter for optimal power extraction. The aim was to design a sensorless controller to improve the system reliability and to simultaneously achieve the regulation of the generator speed, reactive power and DC-link voltage. The proposed global control scheme combines: (i) a high-gain observer employed to estimate the generator speed and the mechanical torque, usually regarded as accessible, (ii) a sensorless MPPT block developed to provide optimal generator speed reference, which is designed on the basis of the mechanical observer and a polynomial wind-speed estimator and (iii) a finite-time controller (FTC) applied to the B2B converter to meet the output reference’s tracking objectives in a short predefined finite time by using the backstepping and Lyapunov approaches. The proposed controller performance is formally analysed, and its capabilities are verified by numerical simulations using a 2 MW DFIG wind turbine (WT) under different operating conditions. Full article

A multi-rate parallel real-time simulation method based on FPGA–CPU is studied to realize the asynchronous co-simulation of the converter of doubly fed wind power systems with the wind turbine and external power grid. The doubly fed wind power system is partitioned by simulation step length, and the partitioned small-step-length data are processed using integral homogenization. For large-step data, an improved delay-compensated linear interpolation method combined with Newton interpolation is proposed for processing. The general small time-step (GST) model method is used to implement the FPGA modeling of the small-step converter, and resource optimization is achieved through timing time-division multiplexing. Asynchronous parallel co-simulation of a doubly fed wind power system is implemented on an FPGA–CPU co-simulation platform. Among them, the FPGA realizes the development of the converter HDL with a small step of 1 $\mathsf{\mu }\mathrm{s}$, while the CPU completes the simulation of the wind turbine and power grid synchronously with a large step of 50 $\mathsf{\mu }\mathrm{s}$. Finally, by comparing with MATLAB/Simulink offline simulation and analyzing the error, it is concluded that the simulation accuracy of the improved method in this paper is higher than that of the un-interpolated parallel simulation, which verifies the real-time performance and accuracy of the modeling and improved method in this paper. Full article

Guideway hydrostatic bearings with the function of supporting and moving loads are a key component of ultra-precision heavy-duty machine tools. Because the dimension difference between the oil gap and the overall structure is great, it is difficult to generate the three-dimensional mesh, which has limited the improvement of bearing performance through structural innovation. To solve these problems, we propose an approach using the global fluid domain for performance analysis. The grid skewness of the film region and other regions are less than 0.4 and 0.8, respectively, which can satisfy the demands of static and dynamic high-accuracy simulation. Then, we used supporting load capacity, stiffness and damping to analyze the performance of hydrostatic bearings. The average error between the simulation result and the actual value was 10.76%, which is better than the result calculated by the traditional empirical formulae. The stiffness and damping of the bearings are easy to obtain by application of dynamic mesh technology. Furthermore, many obvious vortices were shown by visualization analysis in the bearing internal flow pattern in the bearing moving state of 400 mm/s. Finally, a specially designed double-slit septum successfully suppressed the formation of visible vortices. This structural improvement, combining the advantages of deep and shallow recesses, is expected to make hydrostatic bearings at high-speed conditions more stable for ultra-precision machine tools. Full article

Compared to the proportional-integral strategy, the repetitive control strategy possesses high suppression ability for the alternating current (AC) harmonics of control signals. Thus, RC controllers are widely used in closed-loop control systems to suppress the periodic harmonics. In order to further improve the brushless DC (BLDC) motor operation performance, a frequency adaptive repetitive controller (FARC) is proposed, and then a novel current loop scheme that concatenation of proportional-integral controller (PIC) and FARC controller is established in this paper. Firstly, due to the real sampling number of the delay element in the BLDC, the motor control system may not be an integer, the designing process of the FARC parameters was studied, and an adaptive internal model controller and a novel decomposition rule for FARC were designed based on Lagrange interpolation theory. Secondly, the PIC parameters were analyzed through three-dimensional and two-dimensional images of the frequency characteristics. Furthermore, a composite controller that added a forward channel in the novel current loop was proposed, and the stability of the control system used the composite controller was analyzed through Lyapunov theory. It should be noted that the analysis of FARC mainly focused on the simplified structure and the parameter optimization, which is usually ignored in the previous studies. Finally, the BLDC motor control system model was established through Matlab/Simulink software, and the operation performances of the BLDC motor control system utilizing different current loop controllers were studied. The simulation results show that the proposed FARC can reduce current distortion and torque ripples, thus, the BLDC motor operation performances can be improved effectively. Full article

Simultaneous localization and mapping (SLAM) systems play an important role in the field of automated robotics and artificial intelligence. Feature detection and matching are crucial aspects affecting the overall accuracy of the SLAM system. However, the accuracy of the position and matching cannot be guaranteed when confronted with a cross-view angle, illumination, texture, etc. Moreover, deep learning methods are very sensitive to perspective change and do not have the invariance of geometric transformation. Therefore, a novel pseudo-Siamese convolutional network of a transformation invariance feature detection and a description for the SLAM system is proposed in this paper. The proposed method, by learning transformation invariance features and descriptors, simultaneously improves the front-end landmark detection and tracking module of the SLAM system. We converted the input image to the transform field; the backbone network was designed to extract feature maps. Then, the feature detection subnetwork and feature description subnetwork were decomposed and designed; finally, we constructed a convolutional network of transformation invariance feature detections and a description for the visual SLAM system. We implemented many experiments in datasets, and the results of the experiments demonstrated that our method has a state-of-the-art performance in global tracking when compared to that of the traditional visual SLAM systems. Full article

In this paper, we present a non-destructive testing (NDT) technique based on in situ detection of defects up to 100 °C by ultrasonic testing (UT) during construction of parts by a metal additive manufacturing technology known as wire arc additive manufacturing (WAAM). The proposed technique makes use of interlayer application of commercial solder flux to serve as coupling medium for in situ inspection using a special-purpose UT probe. The experimental work was carried out in deposited ER5356 aluminum straight walls following a threefold structure. First, characterization tests with geometrically similar walls with and without interlayer application of solder flux highlight its neutrality, with no effect on the chemical, metallurgical and mechanical properties of the walls. Secondly, UT tests on walls at temperatures ranging from room temperature to 100 °C demonstrate the satisfactory performance of the solder flux as a coupling medium, with little to no soundwave amplitude losses or noise. Finally, acoustic attenuation, impedance and transmission estimations highlight the effectiveness of the proposed technique, establishing a basis for the future development of automated NDT systems for in situ UT of additive manufacturing processes. Full article

In order to explore the influence of airframe space motion on the load sharing performance (LSP) of planetary gear transmission (PGT) inside an aircraft, the combined influence of the rotation of the carrier (internal non-inertial system, INIS) and space motion of the fuselage (external non-inertial system, ENIS) is considered. Firstly, the motion equations of PGT members under the airframe loop maneuver are derived, and then the time-varying meshing stiffness, backlash, and tooth profile error are considered; a system-level dynamics model of PGT with casing is established and solved by numerical integration. The results show that the loop maneuver intensifies the fluctuation of the dynamic meshing force in the time domain and does not change its spectral distribution, but it will affect the peak value of the low frequency part. After considering the loop maneuver, there are differences in the load sharing coefficient (LSC) of each branch, and the fluctuation range of LSC increases. The interactive influence of working conditions, bearing stiffness, manufacturing eccentricity errors and loop motion parameters on the LSP is investigated and compared in depth. This research can be utilized to guide the design of aircraft’s drivetrain. Full article

AC contactors are used frequently in various low-voltage control lines, so remaining-life prediction for them can significantly improve the operational reliability of power control systems. To address the problem that the existing AC contactor remaining-life prediction methods do not make full use of the correlation between previous and later states in the degradation process, a CNN-GRU (convolutional neural network-gated recurrent unit) method for AC-contactor remaining-life prediction is proposed. Firstly, the entire cycle of an AC contactor’s degradation data is obtained through a whole-life test, from which the characteristic parameters that effectively reflect the operating states of the contactor are extracted; secondly, neighborhood component analysis (NCA) and maximal information coefficient (MIC) are used to eliminate the redundant information of multidimensional parameters in order to select the optimal feature subset; and then, CNN is used to compress the feature dimension and mine the regular information between the features, so as to extract the effective feature vectors; finally, taking the AC contactor remaining electrical life as a long time sequence issue, time-series accurate prediction is performed using GRU. It is verified that this model is better than RNN (recurrent neural network), LSTM (long short-term memory) and GRU models in prediction, with an effective accuracy of 96.63%, which effectively supports the feasibility of time-series prediction in the field of the remaining-life prediction of electrical devices. Full article

With the continuous improvement of the imaging quality requirement of the space optical system, the large-aperture mirror becomes the research focus. However, the increase of the aperture will increase the whole weight which results in high launch cost and degrades the mirror surface figure accuracy. Therefore, the lightweight design method of the mirror structure is of great importance. In recent years, many space telescope system schemes have demonstrated the progress of the structural lightweight design of mirrors, such as Spitzer, SOFIA, JWST, etc. This article reviews the main content and innovations of the research on the structural designs of mirrors including conventional machining designs and topology optimization structures. Meanwhile, some emerging designs (e.g., lattices and Voronoi structures) considering additive manufacturing (AM) are also introduced. Several key elements of different structural design approaches for lightweight mirrors are discussed and compared, such as material, lightweight ratio, design methods, surface figure, etc. Finally, future challenges, trends, and prospects of lightweight design for mirrors are discussed. This article provides a reference for further related research and engineering applications. Full article

Product design is a promising field for the application of new technologies and methodologies emerging from the digital evolution of Industry 4.0. A great number of tools have been developed in order to accentuate the use of modern Computer-Aided Design (CAD) systems and computational design techniques for design customization in product applications. The present paper deals with the development of two different applications for designing furniture based on the Computational-based Visual Brand Identity (CbVBI) design methodology. For the first case study, the Application Programming Interface (API) Solidworks^TM (VBA event-driven programming language) is used. The second case study focuses on the visual programming language of Grasshopper^TM, which is incorporated within Rhinoceros3D^TM. The proposed case studies offer a great deal of flexibility in both design and manufacturing, while many design alternatives could become available in a very short period. Full article

Robotic rehabilitation of the lower limb exoskeleton following neurological injury has proven to be an effective rehabilitation technique. Developing assistive control strategies that achieve rehabilitative movements can increase the potential for the recovery of the motor coordination of the participants. In this paper, the innovative contributions are to investigate a robust sliding mode controller (SMC) with radials basis function neural network algorithm (RBFNN) compensator for a novel compliance tendon–sheath actuation lower limb exoskeleton (CLLE) to provide intrinsic thigh and shank rehabilitation training. The controller employing the RBFNN compensator is proposed to reduce the impact of friction from the compliance tendon–sheath actuation system (CTSA). In the design of the compensator, a single parameter is investigated to replace the weight information of the neural network. Our proposed controller is shown to yield fast, stable, and accurate control performance regardless of uncertainties interaction. Two additional algorithms, including a robust adaptive sliding mode controller (RASMC) and a sliding mode proportional-integral controller (SMPIC), are introduced in this paper for comparison. The simulations were presented with MATLAB/SIMULINK to validate the superiority of the performance of the proposed controller. Full article

During the operation process of high-voltage insulators, the characteristics of assessing their technical state are evaluated by using remote contactless monitoring and by subsequent forecasting of their residual life based on the developed set of diagnostic parameters of critical defects. Special attention is paid to the challenges of the practical application for remote contactless monitoring of high-voltage insulators’ current operating state. Measurements of characteristics for partial discharges on high-voltage insulators with various types of critical defects taken by electromagnetic and acoustic sensors are described. Based on the measurements, it was found that the unusual properties of the PD begin to manifest themselves starting from the intensities q ≥ 1.5–2 nC, and their maximum intensity can reach 5–7 nC. Up to PD intensities q ≤ 3 nC, most parameters of PD characteristics measured by electromagnetic and acoustic sensors correspond with an accuracy of 70–90%. It was found that for small defects with sizes d ≤ 300 μm in HVI, the PD intensity does not exceed 100 pC and depends little on the size of the defect. However, with an increase in the size of defects above 0.4–0.6 mm, a sharp rise in the intensity of the emerging VLPD begins. Full article

The rolling element bearing is a fundamental component of any rotating machinery. During operation, wear debris and lubricant impurities create dents and bumps on the bearing raceway surfaces. Such localized defects produce transient vibration impulses at one of the bearing characteristic frequencies. Having a combination of multiple types of point defects on the raceway results in superimposed vibration patterns, which reduce the ability to recognize these defects’ effects. In this paper, a 6-DOF dynamic model is developed to accurately investigate the vibration characteristic of a ball bearing with a multipoint defect comprising a dent and bump on its raceway surface. The model considers the effects of time-varying contact force produced due to defects, lubricant film damping, bearing preload, and the inertia effect of rolling elements. The simulation results reveal the vibration behavior of multipoint defect bearings. In addition, bearing vibration response is affected by the number of defects, the angle between them, and the type and size of each defect. Furthermore, it is challenging to predict bearing defects parameters such as the numbers, types, sizes, and angles between adjacent defects from acceleration signal analysis without jerk signal analysis. The validation of the model is proved using signals from the Case Western University test setup. Full article

Rehabilitation robots, as representative advanced modern rehabilitation devices, are automatically operated machines used for improving the motor functions of patients. Research on rehabilitation robots is typically multidisciplinary research involving technical engineering, clinical medicine, neural science, and other disciplines. Understanding the emerging trends and high-impact publications is important for providing an overview of rehabilitation robot research for interested researchers. Bibliometric analysis is the use of statistical methods to analyze publications over a period of time, which can provide visual insights into the relationships between studies and their publications. In this study, we used “rehabilitation robot*” as a topic term to collect 3527 papers from Web of Science in 127 subject categories published between 2000 and 2019. Rehabilitation robot research has increased rapidly over the past 20 years, 10 key clusters of which were analyzed in this narrative review: improving functional ability after stroke, spinal cord injury, universal haptic drive, robotic-assisted treadmill therapy, treadmill training, increasing productivity, custom-designed haptic training, physical treatment strategies, arm movement therapy, and rehabilitation robotics. Based on this database, we constructed co-citation and co-occurrence networks that were characterized by betweenness centrality values of more than 0.08 and citation bursts with strengths of more than 23, thereby visualizing the emerging trends in the research of rehabilitation robots. Full article