Recognition and practice on remanufacturing engineering management of military aero-engines

The technologies of design, manufacture and MRO （maintenance, repair and overhaul） tor the mili- tary aero-engines, particularly for the fighters, are much more difficult to be mastered than those for any other aero-engines. They have been monopolized by only a few countries and the core technologies have never been transferred due to high risk, high investment, high barrier and high cost. Therefore, our country has long relied on mapping and copying the others＇ engines to satisfy the domestic demand due to not having our own indepen- dent technologies for the design and manufacture of military aero-engines. However, through over 20 years of unremitting effort, the independent MRO has been achieved for all the Chinese fighter engines, covering in- R＆D （research and development） and in-service engines; and the MRO technologies, capabilities, quality, cost-efficiency have reached or exceeded those of original manufacturers. It has grown out of nothing, and then from weak to strong. In particular, dozens of projects concerning the R＆D and engineering application of re- manufacturing have obtained the independent intellectual property rights and are playing an irreplaceable role in achieving leaping improvement of independent MRO for Chinese military aero-engines.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

An Inter-Shaft Bearing Fault Diagnosis Dataset from an Aero-Engine System

In this paper,the aero-engine test with inter-shaft bearing fault is carried out,and a dataset is proposed for the first time based on the vibration signal of rotors and casings.First,a test rig based on a real aero-engine is established,driven by motors and equipped with a lubricating system.Then,the aero-engine is disassembled and assembled following the specification process,and the inter-shaft bearing with artificial fault is replaced.Next,the aero-engine test is conducted at 28 groups of high-and low-pressure speeds.Six measuring points are arranged,including two displacement sensors to test the displacement vibration signals of the low-pressure rotor and four acceleration sensors to test the acceleration vibration signals of the casing.The test results are integrated into an inter-shaft bearing fault dataset.Finally,based on the dataset in this paper,frequency spectrum,envelope spectrum,CNN,LSTM,and TST are used for fault diagnosis,and the results are compared with those of CWRU and XJTU datasets.The results show that the characteristic fault frequency cannot be found directly in the spectrum and envelope spectrum corresponding to this paper’s dataset but in CWRU and XJTU datasets.Using CNN,LSTM,and TST for fault diagnosis of the dataset in this paper,the accuracy is 83.13%,85.41%,and 71.07%,respectively,much lower than the diagnosis results of CWRU and XJTU datasets.It can be seen that the dataset in this paper is closer to the actual fault diagnosis situation and is a more challenging dataset.This dataset provides a new benchmark for the validation of fault diagnosis methods.Mendeley data:https://github.com/HouLeiHIT/HIT-dataset.

MTTSNet:Military time-sensitive targets stealth network via real-time mask generation

The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.

Ultra reliability and massive connectivity provision in integrated internet of military things(IoMT)based on tactical datalink

One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.

RESEARCH AND DEVELOPMENT OF HIGH TEMPERATURESTRUCTURAL MATERIALS FOR AERO-ENGINE APPLICATIONS

The status of research, development of superalloys and materials processing ＆ fabrication technologies for aero-engine applications in China Aviation Industry, with an emphasis on recent achievements at BIAM including directionally solidified and single crystal superalloys for blade and vane applications, wrought superqlloys for aero-engine disks and rings, and powder metalurgy （PM） superalloys for high performance disk applications were described. It was also reviewed the development of new class of high temperature structural materials, such as structural intermetallics, and advanced material processing technologies including rapid solidification, spray forming and so on. The trends of research and development of the above mentioned superalloys and processing technologies are outlined. Cast, wrought and PM superalloys are the workhorse materials for the hot section of current aero-engines. New high temperature materials and advanced processing technologies have been and will be the subject of study. It is speculated that high performance, high purity and low cost superalloys and technologies will play key roles in aero-engines.

Convergence Analysis of the Numerical Solution for Cathode Design of Aero-engine Blades in Electrochemical Machining

As a main difficult problem encountered in electrochemical machining （ECM）, the cathode design is tackled, at present, with various numerical analysis methods such as finite difference, finite element and boundary element methods. Among them, the finite element method presents more flexibility to deal with the irregularly shaped workpieces. However, it is very difficult to ensure the convergence of finite element numerical approach. This paper proposes an accurate model and a finite element numerical approach of cathode design based on the potential distribution in inter-electrode gap. In order to ensure the convergence of finite element numerical approach and increase the accuracy in cathode design, the cathode shape should be iterated to eliminate the design errors in computational process. Several experiments are conducted to verify the machining accuracy of the designed cathode. The experimental results have proven perfect convergence and good computing accuracy of the proposed finite element numerical approach by the high surface quality and dimensional accuracy of the machined blades.

Machining the Integral Impeller and Blisk of Aero-Engines:A Review of Surface Finishing and Strengthening Technologies

The integral impeller and blisk of an aero-engine are high performance parts with complex structure and made of difficult-to-cut materials. The blade surfaces of the integral impeller and blisk are functional surfaces for power transmission, and their surface integrity has signif- icant effects on the aerodynamic efficiency and service life of an aero-engine. Thus, it is indispensable to finish and strengthen the blades before use. This paper presents a comprehensive literature review of studies on finishing and strengthening technologies for the impeller and blisk of aero-engines. The review includes independent and inte- grated finishing and strengthening technologies and dis- cusses advanced rotational abrasive flow machining with back-pressure used for finishing the integral impeller and blisk. A brief assessment of future research problems and directions is also presented.

Aero-engine Blade Fatigue Analysis Based on Nonlinear Continuum Damage Model Using Neural Networks

Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.

Application of multi-outputs LSSVR by PSO to the aero-engine model

Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.

Aero-engine Thrust Estimation Based on Ensemble of Improved Wavelet Extreme Learning Machine

Aero-engine direct thrust control can not only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance.However,it is a big challenge to estimate engine thrust accurately.To tackle this problem,this paper proposes an ensemble of improved wavelet extreme learning machine(EW-ELM)for aircraft engine thrust estimation.Extreme learning machine(ELM)has been proved as an emerging learning technique with high efficiency.Since the combination of ELM and wavelet theory has the both excellent properties,wavelet activation functions are used in the hidden nodes to enhance non-linearity dealing ability.Besides,as original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes,particle swarm optimization(PSO)algorithm is adopted to select the input weights and hidden biases.Furthermore,the ensemble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust.The simulation results verify the effectiveness and efficiency of the developed method and show that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time.

Bifurcation analysis of aero-engine's rotor system under constant maneuver load

When an aircraft is hovering or doing a dive-hike flight at a fixed speed, a constant additional inertial force will be induced to the rotor system of the aero-engine, which can be called a constant maneuver load. Take hovering as an example. A Jeffcott rotor system with a biased rotor and several nonlinear elastic supports is modeled, and the vibration characteristics of the rotor system under a constant maneuver load are analytically studied. By using the multiple-scale method, the differential equations of the system are solved, and the bifurcation equations are obtained. Then, the bifurcations of the system are analyzed by using the singularity theory for the two variables. In the EG-plane, where E refers to the eccentricity of the rotor and G represents the constant maneuver load, two hysteresis point sets and one double limit point set are obtained. The bifurcation diagrams are also plotted. It is indicated that the resonance regions of the two variables will shift to the right when the aircraft is maneuvering. Furthermore, the movement along the horizontal direction is faster than that along the vertical direction. Thus, the different overlapping modes of the two resonance regions will bring about different bifurcation modes due to the nonlinear coupling effects. This result lays a theoretical foundation for controlling the stability of the aero-engine＇s rotor system under a maneuver load.

H∞ Tracking Control for Switched LPV Systems With an Application to Aero-Engines

This paper focuses on the H_∞ model reference tracking control for a switched linear parameter-varying(LPV)model representing an aero-engine. The switched LPV aeroengine model is built based on a family of linearized models.Multiple parameter-dependent Lyapunov functions technique is used to design a tracking control law for the desirable H_∞ tracking performance. A control synthesis condition is formulated in terms of the solvability of a matrix optimization problem.Simulation result on the aero-engine model shows the feasibility and validity of the switching tracking control scheme.

Failure Assessment of Aero-engine Support Structure due to Blade-off

Aero-engine blade-off event could cause serious malfunction and endanger flight safety,which is an important issue widely concerned for a long period.This paper presents a comprehensive review on the regulation requirements,the major research methods and status at home and abroad.Firstly,the relevant certification regulations and standards about aero-engine structure safety due to blade-off event were overviewed and the research gaps between the abroad and the domestic were compared.Then,the simulation and experimental methodologies on aero-engine supporting structures undertake abnormal load due to blade-off event were discussed as major issue.Finally,the safety certification verification technology system for aero-engine support structures during blade-off event was proposed.

Reliability Based Design Optimization of Aero-Engine Spindle Ball Bearings

Aero-engine spindle ball bearings work in harsh conditions which are affected by relatively complex stresses. One of the key factors which affects bearing performance is its structure. In this paper,we used reliability based design optimization method to solve the structure design problem of aero-engine spindle ball bearings.Compared with the optimization design method, the value of equivalent dynamic load using reliability optimization design method was the least by MATLAB simulation. Also the design solutions show that the optimized structure possesses higher reliability than the original solution.

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.

Pushing the frontiers of military medical excellence:updates,progress and future needs

Since its establishment in 2014,Military Medical Research has come a long way in becoming a premier journal for scientific articles from various different specialties,with a special emphasis on topics with military relevance.The field of military medicine may be obscure,and may not be readily encountered by the typical clinician on a day-today basis.This journal aims not only to pursue excellence in military research,but also to keep current with the latest advancements on general medical topics from each and every specialty.This editorial serves to recap and synthesize the existing progress,updates and future needs of military medical excellence,discussing foremostly the unique traits of literature published in this journal,and subsequently presenting the discourse regarding wartime and peacetime medicine,the role of the military in a public health emergency,as well as wound healing and organ regeneration.Special attention has been devoted to military topics to shed light on the effects of Chemical,Biological,Radiological and Explosive warfare,environmental medicine and military psychiatry,topics which rarely have a chance to be discussed elsewhere.The interconnectedness between military combat and soldier physical and mental well-being is intricate,and has been distorted by pandemics such as coronavirus disease 2019(COVID-19).This journal has come a long way since its first article was published,steadily contributing to the existing knowledge pool on general medical topics with a military slant.Only with continuous research and sharing,can we build upon the work of the scientific community,with hopes for the betterment of patient care.

Research of vibrations effect on hydraulic valves in military vehicles

The paper discusses minimizing the effect of external mechanical vibration on hydraulic valves in different military hydraulic drive systems.The current research work presents an analysis of the potential to reduce vibration on the valve casing by installing a valve flexibly on a vibrating surface,i.e.,by introducing a material with known stiffness and damping characteristics between the valve casing and the vibrating surface-a steel spring package or special cushions made of elastomer material or of oilresistant rubber.The article also demonstrates that elastomer cushions placed inside the valve casingbetween the casing and the centering springs-can be used as a supplementary or alternative solution in the analyzed method for mitigating the transfer of vibrations.By using materials with appropriately selected elastic and dissipative properties,the effectiveness of vibro-isolation can be increased.The presented theoretical analyzes by linear and non-linear mathematical models have been verified experimentally.

Power System Analysis of an Aero-Engine

The aim of this study is analyzed in detail for better understanding of energy and power of an aero-engine. In this regard, this study presents energy equations were applied to the turbofan engine components. The engine has a thrust range of 82 to 109 kN. It consists of fan, axial low pressure compressor (LPC), axial high pressure compressor (HPC), an annular combustion chamber, high-pressure turbine (HPT) and low pressure turbine (LPT). The results show that power of the engine flow approaches a maximum value to be 82.85 MW in the combustor outlet, while minimum power is observed at LPC inlet with the value of 1.37 MW. Furthermore, important parameters of the engine are also analyzed from reverse-engineering method. It is expected that results of this study will be beneficial of power, cogeneration and aero-propulsive generation systems in similar environment.

Lethal means and suicide prevention among military veterans

Dear Editor,Suicide amongst the military veteran population is a significant publichealthproblemintheUnitedStates.TheNational VeteranSuicidePreventionAnnualReportrevealedthat6261 died by suicide in 2019[1]. The lingering effects of the coronavirus disease 2019 (COVID-19) pandemic may account for an increase in veteran suicide rates[1].