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Abstract As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

Commercial vehicle disk brakes operate at a high-temperature and in a heavy-load environment within the braking system. The primary cause of failure is the cracking of the brake disk. In order to study its fatigue damage and service life, a finite element model of disk brake fatigue life was established, and thermal stress coupling simulation analysis was carried out from a practical problem. Based on the temperature and stress fields of the brake disk under emergency braking conditions obtained from the simulation results, the effects of vehicle load, initial speed, temperature, and other factors on brake fatigue life are explored. The fatigue life of the hazardous node can be calculated using the Manson–Coffin model, and then the strain–life (ɛ–N) curve of the material can be fit at high temperature. The fatigue life of brake disks was predicted using the fatigue analysis software FE-SAFE and verified by testing. The results showed that the maximum stress on the surface of the disk brake was the same as the area of the minimum fatigue life, accurately analyzing the fatigue life of the region and predicting the location of fatigue cracks. The results of the research can provide a reference for the design of disk brake engineering and fatigue failure.

A 5-DOF series-parallel robot is designed based on the single-opened-chain and position and orientation characteristic set， and the configuration synthesis and degrees of freedom of the 5-DOF series-parallel robot are calculated and analyzed to verify that the proposed mechanism meets the design requirements. The kinematics of the 5-DOF series-parallel robot is further discussed， and the position inverse solution of the parallel mechanism and the speed of the moving platform are analyzed. The Adams software is used to simulate the designed 5-DOF series-parallel robot to verify the feasibility of the mechanism. The results show that the proposed mechanism has the characteristics of relatively stable motion and large working space， which provides a reference basis for the multi-drive control of the 5-DOF series-parallel mechanism.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Health state prediction is an effective way to improve the reliability for sensors. In the process of sensor degradation, it is difficult to obtain more effective monitoring data. And in the classification of health states, how to identify the adjacent state is also a problem. This paper proposed a health state prediction model based on belief rule base (BRB) and membership function (MF), which is called BRB-MF. In the model, BRB can make full use of expert knowledge and poor effective data. In the prediction results of BRB, it may be not completely logical or not entirely appropriate facing adjacent states of sensor. In order to solve the problem, MF is used to continue the analysis of the predicted results of BRB. In the BRB-MF model, the covariance matrix adaptation evolutionary strategies (CMA-ES) optimization algorithm is used to update the model parameters to make up for the uncertainty of expert knowledge. In the end, the brightness sensor of the rail vehicle LED lighting system is taken as a case study. The results show that the BRB-MF model can predict the health state of sensor with a high accuracy and a reasonable state.

In this study, a model predictive path tracking control method based on the prediction of tire state stiffness is proposed to improve the path tracking performance at the limit of vehicle dynamics. Considering the influence of the nonlinear properties of tire force on vehicle dynamics, a nonlinear UniTire model is established, based on which a state stiffness 3D look-up table is designed to linearize the nonlinear tire model. The tire state stiffness in the prediction horizon is predicted by the vehicle motion model using the reference path information. A new linear time-varying path tracking control model in the prediction horizon is designed based on the predicted tire state stiffness. A nonlinear model predictive controller and a traditional linear time-varying model predictive controller are also designed and compared with the proposed method to verify the effectiveness and advantage of the latter. Results clearly show an improved control performance of the proposed method compared with the traditional method under the limit condition. Moreover, the calculation speed of the proposed method is faster than that of the nonlinear model predictive control method.

The running gear is a vital component of a high-speed train to ensure operation safety. Accurately predicting the future health status of the running gear is significant to keep the reliability and safety of trains. It is difficult to predict the future health status based on the analytical model of the running gear system because of its complexity and coupling. Moreover, the fault data are a minor part of tremendous data in the running and monitoring process of a high-speed train, which obstructs accurately predicting the health status based on a data-driven method. To solve the above problems, this paper proposes a health status prediction method based on the belief rule base (BRB) for the running gear system. First, a failure mechanism is analyzed to confirm the fault characteristics, which can represent the health status of the running gear system. Second, in order to avoid the limitations of a single sensor acquisition, such as a lack of comprehensiveness and robustness, singular value decomposition is used to achieve multisensory information fusion. The fused features are used as the input to the health status prediction model. Data fusion is a way to improve the precision of the health status prediction in the model input. Then, this model based on the BRB is established using the fault data and expert knowledge. During the process of prediction, the subjectivity of experts makes the initial BRB imprecise, so a projection constrained covariance matrix adaptive evolution strategy algorithm is needed to optimize the initial parameters and improve the accuracy of the proposed model. Finally, a case study for the running gear system is carried out to verify the effectiveness and accuracy of the proposed model. The results show that the proposed model can help to accurately predict the health status of the running gear system.

Model predictive control (MPC) is advantageous for designing an electrical vehicle path-tracking controller, but the high computational complexity, mathematical problem, and parameterization challenge adversely affect the control performance. Hence, based on a fully actuated-by-wire electrical vehicle (FAW-EV), a novel path-tracking controller based on improved MPC with a Laguerre function and exponential weight (LEMPC) is designed. The massive optimization control parameters of MPC with a long control horizon are reduced by introducing a fitting orthogonal sequence consisting of Laguerre functions, thereby substantially reducing the computational complexity without sacrificing the tracking accuracy. An exponential weight with decreasing characteristic is introduced to MPC to solve the mathematical problem, thereby improving the robustness of the path tracking controller. In addition, the parameterization access for online adjusting path tracking control performance can be provided by the proposed method. The path tracking motion realization for FAW-EV is subsequently illustrated. Finally, several simulations are implemented to verify the advantages of the proposed method.

This paper proposes a vehicle stability control approach based on time-varying model predictive control to enhance the handling and stability of active front steering vehicle at the vehicle dynamics limits. The prediction equation of the proposed controller is designed based on the changing trend of tire force over the prediction horizon. Therefore, the prediction equation can represent the nonlinear characteristics in the process of prediction. To verify the effectiveness of the proposed control strategy, nonlinear model predictive controller, and previous linear time-varying model predictive controller are designed and used for comparison. Simulation experiments are performed based on the cosimulation environment of MATLAB and CarSim. At the handling limits, the control performance of the proposed controller exhibited significant improvement compared with the previous one. Moreover, its performance was close to that of the nonlinear controller, whereas its calculation speed is much faster than that of the nonlinear model predictive controller.

The proper operation of running gears of a high-speed train is one of the key factors to ensure its safety and reliability. The diagnosis of the state of running gears of a high-speed train is one of the effective ways to improve its reliability. It is difficult to diagnose the running gears of a high-speed train accurately because of the characteristics of its complex-analytic structure, multiple types of monitoring feature data, and lack of effective failure mode data. Therefore, this paper proposes a fault diagnosis method for the running gears of a high-speed train based on a semi-quantitative information model. The relation between the effective data and expert knowledge is studied, and the state of the running gears of a high-speed train is rigorously analyzed. To reduce the data dimension and the diagnostic calculation time of the running gears of a high-speed train, the principal component analysis (PCA) is used to screen its key monitoring features. Then, based on the change of the feature quantity in the working process of the running gears of a high-speed train, the semi-quantitative information model of belief-rule-base (BRB) fault diagnosis is established. In the diagnosis process, the initial model parameters of BRB are determined by expert knowledge and they have certain subjectivity. To improve the accuracy of the model, the constrained covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the parameters of the initial BRB model to improve the validity and accuracy of the diagnosis. Finally, to verify the effectiveness of the proposed semi-quantitative information model, a set of real data of the running gears of a high-speed train is used as case studies.

In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.

In this paper, a three-order Taylor-type numerical differentiation formula is firstly utilized to linearize and discretize constrained conditions of model predictive control (MPC), which can be generalized from lower limb rehabilitation robots. Meanwhile, a new numerical approach that projected an active set conjugate gradient approach is proposed, analyzed, and investigated to solve MPC. This numerical approach not only incorporates both the active set and conjugate gradient approach but also utilizes a projective operator, which can guarantee that the equality constraints are always satisfied. Furthermore, rigorous proof of feasibility and global convergence also shows that the proposed approach can effectively solve MPC with equality and bound constraints. Finally, an echo state network (ESN) is established in simulations to realize intention recognition for human–machine interactive control and active rehabilitation training of lower-limb rehabilitation robots; simulation results are also reported and analyzed to substantiate that ESN can accurately identify motion intention, and the projected active set conjugate gradient approach is feasible and effective for lower-limb rehabilitation robot of MPC with passive and active rehabilitation training. This approach also ensures computational when disturbed by uncertainties in system.

The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.

The health of a complex electromechanical system is dynamic and is accompanied by a full life cycle. Due to the complexity and coupling of complex electromechanical systems, the establishment of a dynamic and accurate model for the health state is difficult. A belief rule base (BRB) shows outstanding performance in modeling complex systems because it can combine both quantitative information and expert knowledge. In this paper, a double-layer BRB model is proposed to predict the health state of a complex electromechanical system. The two layers achieve different functions: BRB_layer1 is used to establish the dynamic change of the time series of features, BRB_layer2 is employed to combine the features for predicting the health state of the complex electromechanical system. During this process, the infinite irrelevance method is utilized for feature selection in reducing the scale of the BRB model. Considering the initial parameters are given by experts, which may have boundedness and may not be appropriate for engineering practice, the projection covariance matrix adaption evolution strategy (P-CMA-ES) is chosen as the optimization algorithm to train the initial parameters. To verify the rationality and effectiveness of the proposed model, the low-frequency vibration fault of a certain aero-engine is taken as an example. The results show that the proposed method can predict the health state of a complex electromechanical system precisely according to current and historical data.

Remaining life prediction is an effective way to optimize maintenance strategy and improve service life for light-emitting diode driving power in rail vehicle carriage. In this article, a Wiener process–based remaining life prediction method is proposed with the analysis of performance degradation data of light-emitting diode driving power in rail vehicle carriage. First, the temperature and humidity stress accelerated degradation tests are put forward in order to measure the output current of light-emitting diode driving power. Based on the output current, the accelerated degradation model is established. The drift and diffusion coefficients of the Wiener process are then obtained without prior information. Finally, the reliability of light-emitting diode driving power in rail vehicle carriage is assessed and the remaining lifetime is predicted after updating the degradation model parameters with Bayesian inference. The results show that the proposed method can improve the precision of assessment and reduce the uncertainty of prediction significantly. It also provides a potential solution for life prediction of other similar products.

An air suspension platform uses air pressure to realize the suspension function during the suspension process, and it has the disadvantage of large air pressure and a small suspension force. In this study, an air suspension platform was built using bionic design to reduce the required air pressure and increase the suspension force. A suspension structure mapping model was established according to the physiological structure characteristics of albatross wings. A bionic model was established by using the theoretical calculation formula and structural size parameters of the structural design. A 3D printer was used to manufacture the physical prototype of the suspended workpiece. Based on this, a suspension test rig was built. Six sets of contrast experiments were designed. The experimental results of the suspension test bench were compared with the theoretical calculation results. The results show that the buoyancy of the suspended workpiece with a V-shaped surface at a 15-degree attack angle was optimal for the same air pressure as the other workpieces. The surface structure of the suspended workpiece was applied to the air static pressure guide rail. By comparing the experimental data, the air pressure of the original air suspension guide rail was reduced by 37%, and the validity of the theory and design method was verified.

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

With the advantage of steering performance, articulated tracked vehicles have excellent mobility in off-road application. However, in current models for steering performance, soil deformation on the interaction between track and soil cannot always be taken into account. Therefore, steering performance cannot always be calculated accurately. In order to solve the problem, it is essential to propose a steering model which can take the effect of soil deformation on track–soil interaction into consideration. In this article, a steering model of articulated tracked vehicle is proposed on track–soil interaction. Moreover, in order to improve steering performance, a track–soil sub-model is developed that can consider soil deformation on track–soil interaction. Using this steering model based on track–soil sub-model, steering performance can be calculated more accurately. Simulation studies and experimental results are in strong agreement with the theoretical results in this article. The results show that equipped with the track–soil sub-model, the proposed steering model can be used to accurately predict steering performance. The steering model of articulated tracked vehicle proposed in this article can provide a basis for other similar vehicles.

A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement.

Vibration signal processing is commonly used in the mechanical fault diagnosis. It contains abundant working status information. The vibration signal has some features such as non-linear and non-stationary. It has a lot of interference information. Fault information is vulnerable to the impact of the interference information. Empirical mode decomposition denoising method and kurtosis correlation threshold have been widely used in the field of fault diagnosis. But the method mainly depends on the subjective experience, the large number of attempts, and lack of adaptability. In this article, the signals are decomposed into several intrinsic mode functions adaptively with ensemble empirical mode decomposition. The intrinsic mode functions containing the main fault information are selected by the correlation coefficient to emphasize the fault feature and inhibit the normal information. Finally, the energy features of these intrinsic mode functions are taken as inputs of a neural network to identify the fault patterns of rolling bearing. The experiment shows that the neural network diagnosis method based on ensemble empirical mode decomposition has a higher fault recognition rate than based on empirical mode decomposition or wavelet packet method.

Fan is an important rotating part in turbofan engine gas-path. The health condition of fan has a great impact on the health status of the whole aero-engine. Based on belief-rule-base, a novel health estimation model is proposed for fan in turbofan engine gas-path. In this model, the health condition of fan is reflected by the observable information which can represent the system health. In the process of health estimation, the expert knowledge is used fully to improve the precision and speed of the estimation. In the initial health estimation model, some parameters given by expert may not be accurate. To obtain the accurate estimation result, an algorithm for updating the parameters is proposed based on differential evolution algorithm. In order to verify the feasibility and accuracy of the proposed model, back-propagation neural network is applied to comparison. The newly proposed model is applied to an actual test in the aero-engine test bed, which is used to testify the validity of the health estimation model. This model can also provide a reference for the health estimation of turbofan engine gas-path.

The fault tolerant control problem for a DC motor system is investigated in a cloud environment. Packet dropout phenomenon introduced by the limited-capacity communication channel is considered. Actuator faults are taken into consideration and fault diagnosis and fault tolerant control methods towards actuator faults are proposed to enhance the reliability of the whole cloud-based DC motor system. The fault diagnosis unit is then established with purpose of obtaining fault information. When the actuator fault is detected by comparing the residual signal with a predefined threshold, a residual matching approach is utilized to locate the fault. The fault can be further estimated by a least-squares filter. Based on the fault estimation, a fault tolerant controller is designed to guarantee the stability as well as the control performance of the DC motor system. Simulation result on a DC motor system shows the efficiency of the fault tolerant control method proposed in this paper.

This paper presents a humanoid robotic arm massage system with an aim toward satisfying the clinical requirements of pain relief on the waist and legs of older patients during Chinese medicinal massage. On the basis of an in-depth analysis regarding the characteristics of arm joints of the human body and Chinese medicinal massage theory, a humanoid robotic arm massage system was designed by adapting a bottom to top modular method. The combined finite element and kinematic analysis led to an improved performance according to repeated positioning accuracy, massage strength accuracy, and massage effect. The developed humanoid robotic arm was characterized by a compact structure, high precision, light quality, and good stiffness, achieving a good bearing capacity. Due to the PID controller, the numerical simulations and experimental results provided valuable insight into the development of Chinese medicinal massage robots and massage treatments for patients who suffer from lumbar muscle strain.

Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm are used to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solve the errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data, the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, the effectiveness of the proposed approach is validated on an experimental data relating to micro-switches for the rail vehicle. Additionally, it proposes another two methods for comparison to illustrate the effectiveness of the method with an inverse Kalman filter in this paper. In conclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors in RUL estimation of the micro-switches excellently.

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability. [ABSTRACT FROM AUTHOR]