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Abstract Quantum clock synchronization (QCS) can measure out the high-precision clock difference among distant users, which breaks through the standard quantum limit by employing the properties of quantum entanglement. Currently, the wavelength division multiplexed QCS network has been demonstrated with a spontaneous parametric down-conversion entangled photon source. In this paper, we propose a more efficient QCS network scheme with the wavelength multicasting entangled photon source, which can decrease at least 25% of wavelength channel consumption under the identical network scale. Afterwards, a four node QCS network is demonstrated, where the wavelength multicasting entangled photon source is utilized with dual-pumped four-wave mixing silicon chip. The experimental results show that the measured time deviation is 3.4 ps with an average time of 640 s via the multiple fiber links of more than 10 km.

In recent years, landscape pattern optimization has been regarded as a cutting-edge science in various fields, and basin hydrology is no exception. Its effect on basin water purification has also been focused on. Based on the analysis of land use types and landscape patterns in the Danjiangkou Reservoir Basin, and combined with the determination of water quality indicators in the Danjiangkou Reservoir Basin, the relationship between the spatial pattern of Landscape Morphology and water quality was studied; It is found that among the basic characteristic indicators of many basins, the main influencing factors of water quality with high Variable importance for the projection (VIP>1) are IJI (Interspersion and Juxtaposition Index), COHE (Patch Cohesion Index), AI (Aggregation Index), PD (Patch Density), ED (Edge Density); Using PLSR (Partial Least Squares Regression) analysis, the main influencing factors of water quality were discussed. It was found that there was a significant negative regression relationship (Rc

ObjectivesThe objective of this study was to develop a predictive nomogram for intermediate-risk differentiated thyroid cancer (DTC) patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation (RRA).MethodsData from 265 patients who underwent total thyroidectomy with central lymph node dissection (CND) and received RRA treatment at a single institution between January 2018 and March 2023 were analyzed. Patients with certain exclusion criteria were excluded. Univariate and multivariate logistic regression analyses were performed to identify risk factors for a non-excellent response (non-ER) to RRA. A nomogram was developed based on the risk factors, and its performance was validated using the Bootstrap method with 1,000 resamplings. A web-based dynamic calculator was developed for convenient application of the nomogram.ResultsThe study included 265 patients with intermediate-risk DTC. Significant differences were found between the ER group and the non-ER group in terms of CLNM>5, Hashimoto’s thyroiditis, sTg level, TgAb level (P < 0.05). CLNM>5 and sTg level were identified as independent risk factors for non-ER in multivariate analysis. The nomogram showed high accuracy, with an area under the curve (AUC) of 0.833 (95% CI = 0.770–0.895). The nomogram’s predicted probabilities aligned closely with actual clinical outcomes.ConclusionsThis study developed a predictive nomogram for intermediate-risk DTC patients after fixed 3.7GBq (100mCi) RRA. The nomogram incorporates CLNM>5 and sTg levels as risk factors for a non-ER response to RRA. The nomogram and web-based calculator can assist in treatment decision-making and improve the precision of prognosis information. Further research and validation are needed.

This study aimed to determine the effect of tea planting age on stoichiometric ratios of microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) and soil microbial quotient (SMQ, expressed as qMBC, qMBN, and qMBP, respectively). A chronological sequence of tea plantations (3, 8, 17, 25, and 34 years) was selected in a small watershed in the Three Gorges Reservoir Area, and a slope farmland was selected as control. The results showed that with the increase of tea plantation age, soil and microbial biomass C, N, P contents, soil C:N and C:P elevated significantly, while soil N:P overall declined; the MBC:P and MBN:P increased first and then decreased, but MBC:N varied insignificantly. The tea plantation age affected SMQ notably. qMBC first decreased and then increased following the tea planting age, while qMBN and qMBP went up in a fluctuating pattern. In this study, qMBC positively correlated with soil N:P and microbial biomass C:N:P, but negatively correlated with soil C:N and C:P; on the contrary, qMBN and qMBP negatively correlated with soil N:P and microbial biomass C:N:P, but positively correlated with soil C:N and C:P. Generally, the variations of soil microbial biomass and SMQ could reflect the soil quality of tea plantations.

Abstract Background The cryptochromes (CRY) are specific blue light receptors of plants and animals, which play crucial roles in physiological processes of plant growth, development, and stress tolerance. Results In the present work, a systematic analysis of the CRY gene family was performed on twelve cotton species, resulting in 18, 17, 17, 17, and 17 CRYs identified in five alloteraploid cottons (Gossypium hirsutum, G. barbadense, G. tomentosum, G. mustelinum and G. darwinii), respectively, and five to nine CRY genes in the seven diploid species. Phylogenetic analysis of protein-coding sequences revealed that CRY genes from cottons and Arabidopsis thaliana could be classified into seven clades. Synteny analysis suggested that the homoeolog of G. hirsutum Gh_A02G0384 has undergone an evolutionary loss event in the other four allotetraploid cotton species. Cis-element analysis predicated the possible functions of CRY genes in G. hirsutum. RNA-seq data revealed that Gh_D09G2225, Gh_A09G2012 and Gh_A11G1040 had high expressions in fiber cells of different developmental states. In addition, the expression levels of one (Gh_A03G0120), 15 and nine GhCRY genes were down-regulated following the PEG, NaCl and high-temperature treatments, respectively. For the low-temperature treatment, five GhCRY genes were induced, and five were repressed. These results indicated that most GhCRY genes negatively regulate the abiotic stress treatments. Conclusion We report the structures, domains, divergence, synteny, and cis-elements analyses systematically of G. hirsutum CRY genes. Possible biological functions of GhCRY genes in differential tissues as well as in response to abiotic stress during the cotton plant life cycle were predicted.

Freeze-thaw erosion occurs primarily at high latitudes and altitudes. Temperature controlled freeze-thaw events dislodge soil particles and serve as a catalyst for erosion. This review paper provided an overview of the effects of freeze-thaw on soil properties and water erosion. The process of freeze-thaw cycles results in temporary and inconsistent changes in the soil moisture, and affects the soil's mechanical, physical and chemical properties, such as the soil moisture content, porosity, bulk density, aggregates stability, shear strength and organic matter content and so on. The variation trend and range of the soil properties were related to the soil texture, water content and freeze-thaw degree. Furthermore, the soil erosion was affected by the freeze-thaw processes, as thawing and water erosion reinforce each other. However, research of different experimental conditions on indoor simulations have numerous limitations compared with field experiments. The use of indoor and field experiments to further reveal the freeze-thaw effect on the soil erosion would facilitate improved forecasting.

A novel estimation-based and dropout-dependent control design for distributed control systems of aeroengines with packet dropout is proposed. The packet dropout is described as an independent identically distributed (i.i.d.) Bernoulli process with known probability. The objective of the control system is to effectively and stably impel aeroengines. An inverse system is proposed to reconstruct the missing measurements, based on which, a hybrid adaptive Kalman filter (HAKF) is proposed to provide the estimated states for control design. A new state-feedback control design is developed with the estimated states and the actual states. For guaranteeing the stability of aeroengines, both the stability of HAKF and the new controlled system are analyzed and proved. The new design is applied to an aeroengine, HAKF has superior estimation accuracy which ensures the effectiveness, and the desired stability performance is achieved by controlled system in the presence of packet dropout.

The variable cycle engine switches working modes by way of changing variable-geometry components to achieve the dual advantages of high unit thrust and low specific fuel consumption. Due to the lack of a large amount of rig test data and the complex modeling of rotating components, the incomplete characteristics of the variable-geometry rotating components lead to the non-convergence of the component-level model of the variable cycle engine, which makes it difficult to design the follow-up control system. Aiming at this problem, a characteristics modeling method of variable-geometry rotating components for variable cycle engine based on reference characteristic curves is proposed in this paper. This method establishes a neural network estimation model for the offset coefficients of key component operating points based on the characteristic law of the maturely designed variable-geometry rotating component. Combining the neural network model and the reference characteristic curves of the variable-geometry component to be designed, the offset positions of the operating points for positive and negative guide vane angles are determined. Instead of directly connecting operating points to generate characteristic lines, this paper solves the Bezier curve optimization problem based on sequential quadratic programming (SQP) to smoothly fit characteristic lines. Thereby, component characteristics that conform to the actual variable-geometry characteristics can be quickly established in the absence of rig test data. The simulations show that the characteristics of the variable-geometry rotating components established by the proposed method have satisfactory accuracy and reliability, which further improves the operation stability of the component-level model of the variable cycle engine.

The variable cycle engine selects the appropriate working mode through mode switch to meet the requirements of low specific fuel consumption and high unit thrust. When the variable cycle engine switches between different modes, it should ensure the smooth transition of working modes and obtain the expected performance at the same time. Consequently, a general control schedule design method for mode switch is investigated in this paper. The feasible switching domains of single-bypass working mode and double-bypass working modes are obtained based on multifurcating tree traversal, and the switching point in the feasible switching domain is determined by solving the collaboration-game optimization problem based on sequential quadratic programming (SQP). Finally, the schedule of control variables is designed to realize smooth and fast switching between different working modes of variable cycle engine. The simulations show that based on the general control schedule of mode switch proposed in this paper, smooth transition of thrust and rotor speed can be achieved under the premise of safe engine operation, with small fluctuations and a short switching time, which is about 2.5 s.

Accurate component maps, which can significantly affect the efficiency, reliability and availability of aero-engines, play a critical role in aero-engine performance simulation. Unfortunately, the information of component maps is insufficient, leading to substantial limitations in practical application, wherein compressors are of particular interest. Here, a data-driven-based compressor map generation approach for transient aero-engine performance adaptation is investigated. A multi-layer perceptron neural network is utilized in simulating the compressor map instead of conventional interpolation schemes, and an adaptive variable learning rate backpropagation (ADVLBP) algorithm is employed to accelerate the convergence and improve the stability in the training process. Aside from that, two different adaptation strategies designed for steady state and transient conditions are implemented to adaptively retrain the compressor network according to measurement deviations until the accuracy requirements are satisfied. The proposed method is integrated into a turbofan component-level model, and simulations reveal that the ADVLBP algorithm has the capability of more rapid convergence compared with conventional training algorithms. In addition, the maximum absolute measurement deviation decreased from 6.35% to 0.44% after steady state adaptation, and excellent agreement between the predictions and benchmark data was obtained after transient adaptation. The results demonstrate the effectiveness and superiority of the proposed component map generation method.

Research was conducted at a chicken transfer station to assess ecological treatment and resource utilization. The study examined three aspects: wastewater ecological treatment, resource utilization maximization, and process optimization. Process design and operation monitoring were carried out to treat and reuse wastewater from a chicken feeder station over two periods. The first period was operated in 2014, adopting the mode of pretreatment plus a constructed wetland. Results show that the relevant indicators basically meet the regulatory requirements at that time. The second period carried out in 2017 improved upon the results obtained during the first period. On the basis of strengthening the pretreatment and constructed wetland functions, full recycling of tailwater and zero discharge of wastewater was achieved. The aquatic plant water celery used for wetland wastewater purification function also reached the standard of safe vegetable consumption, producing systematic ecological and economic benefits. The second phase of the project has high promotion and application value in the wastewater treatment of the chicken transfer station. This study demonstrates an improved approach to poultry production wastewater treatment by transforming wastewater into an agricultural product while achieving wastewater reuse and environmental pollution control.

Kernel adaptive filtering (KAF) has gained widespread popularity among the machine learning community for online applications due to its convexity, simplicity, and universal approximation ability. However, the network generated by KAF keeps growing with the accumulation of the training samples, which leads to the increasing memory requirement and computational burden. To address this issue, a pruning approach that attempts to restrict the network size to a fixed value is incorporated into a kernel recursive least squares (KRLS) algorithm, yielding a novel KAF algorithm called parsimonious KRLS (PKRLS). The basic idea of the pruning technique is to remove the center with the least importance from the existing dictionary. The importance of a center is quantified by its contribution to minimizing the cost function. The calculation of the importance measure is formulated in an efficient manner, which facilitates its implementation in online settings. Experimental results on the benchmark tasks show that PKRLS obtains a parsimonious network structure with the satisfactory prediction accuracy. Finally, a multi-sensor health diagnosis approach based on PKRLS is developed for identifying the health state of a degraded aero-engine in realtime. A case study in a turbofan engine degradation data set demonstrates that PKRLS provides an effective and efficient candidate for modeling the performance deterioration of real complex systems.

The Kalman filter (KF) is the most common state estimation method for gas turbine health monitoring, and it runs in the centralized architecture. However, health estimation cannot be achieved by the KF-based method as sensor fault occurs, and malfunction of the central monitoring unit will unavoidably result to the termination of the diagnosis task. For these purposes, this paper develops a novel hybrid federated KF approach from the previous achievements. The hybrid KF consists of a bank of local filters and one master filter, and the federated filtering structure and asynchronous fusion mechanism are designed. Both the linearized KF and extended KF are employed as the local filters based on the linear correlation of thermodynamic parameters. The local state estimates and covariance are yielded in parallel, and then integrated in a master filter to produce global state estimate. The proposed methodology is evaluated and compared with the general federated KFs in terms of estimation accuracy, computational efforts, and robustness to sensor fault in the application of gas turbine health monitoring. The result shows that the hybrid KF is the best balance off the involved performance, and confirms our viewpoints in this paper.

Kalman filter (KF) is a widely used technique to obtain health condition in aero engine health management, and each kind of measurement is commonly assumed to be collected and tackled simultaneously from one sensor in the KF for state tracking in previous studies. However, there are redundant sensor measurements of the same kind employed with different sampling and transmission rate, and it is hard to achieve state estimation using multiple sensors at one time, especially, in the advanced distributed architecture. For these purposes, a multi-sensor asynchronous fusion approach is proposed to track engine health state using distributed cubature information filter (CIF) in this paper. The sequential and distributed integration structures are designed for asynchronous computation, and the square-root cubature rule are combined with the CIF to complete state estimation. The global state estimate of distributed square-root CIF (DSCIF) is obtained from the fusion of local estimates using multi-scale sampling strategy. The performance comparisons of the examined methodologies are conducted to state estimation with asynchronous measurements in aero engine gas path health tracking applications. The results illustrate the superiority of the DSCIF with regards to estimation accuracy and computational efforts, and confirm our viewpoints in the asynchronous filtering estimation for the multi-sensor system.

Aero-engines are faced with severe challenges of availability and reliability in the increasing operation, and traditional gas path filtering diagnostic methods have limitations restricted by various factors such as strong nonlinearity of the system and lack of critical sensor information. A method based on the aerothermodynamic inverse model (AIM) is proposed to improve the adaptation accuracy and fault diagnostic dynamic estimation response speed in this paper. Thermodynamic mechanisms are utilized to develop AIM, and scaling factors are designed to be calculated iteratively in the presence of measurement correction. In addition, the proposed method is implemented in combination with compensation of the nonlinear filter for real-time estimation of health parameters under the hypothesis of estimated dimensionality reduction. Simulations involved experimental datasets revealed that the maximum average simulated error decreased from 13.73% to 0.46% through adaptation. It was also shown that the dynamic estimated convergence time of the improved diagnostic method reached 2.183 s decrease averagely without divergence compared to the traditional diagnostic method. This paper demonstrates the proposed method has the capacity to generalize aero-engine adaptation approaches and to achieve unbiased estimation with fast convergence in performance diagnostic techniques.

A robust output tracking controller is necessary for the safe and reliable operation of aeroengines. This paper aims at developing an H 2 / H ∞ output tracking approach for aeroengines. In order to improve the tracking performance of the traditional robust tracker, the proposed control structure is designed as a combination of a nominal controller and a compensator. Concretely, an H 2 / H ∞ nominal controller is derived from game algebraic Raccati equation (GARE), which facilitates establishing a compensator for the system. Since the reference is usually unknown in advance for practical application, the proposed compensator is calculated online according to the nominal controller and the current reference. The solvability of the compensator and the stability of the system is guaranteed for both stable and bounded unstable references. Simulation examples for a turbofan engine are provided to demonstrate the effectiveness of the proposed algorithm.

This paper investigated the problem of fault estimation and fault-tolerant control (FTC) against sensor faults for aircraft engines. By applying a second order sliding mode observer (SOSMO) to the engine on-board model, estimations of the system states and sensor faults could be obtained simultaneously, and the result of state estimation was unaffected when using the reduced-order sliding mode system. This result gave rise to the idea to use the estimated states instead of physical sensor signal in the engine close-loop feedback control. Unlike those using passive FTC concepts, the tradeoff between control performance and robustness was inherently unnecessary. Meanwhile, compared to active FTC approaches, because any classical state/output feedback method can be directly applied to the proposed scheme without any controller reconfiguration, extra undesired dynamic responses caused by parameter reconfiguring were avoided. In this paper, the proposed FTC scheme was tested on the nonlinear model of a civil aircraft turbofan engine, and numerical simulation results showed satisfactory sensor FTC performance.

To accomplish the limit protection task, the Min-Max selection structure is generally adopted in current aircraft engine control strategies. However, since no relationship between controller switching and limit violation is established, this structure is inherently conservative and may produce slower transient responses than the behavior by engine nature. This paper proposes an output-based limit management strategy, which consists of the safety margin module and the parameter prediction module to monitor system responses, plus the switching logic to govern switches between the main controller and limiters, and, in this way, a faster transient performance is achieved, and the limit protections in transient states become more effective. To realize smooth switching control, the linear-quadratic bumpless transfer method is developed. The design principle of the multi-loop switching control and bumpless compensator is detailed, and the effect—on limit protection control performance—of the design parameters in the safety margin and parameter prediction modules are also analyzed. The proposed approach is tested using simulations covering the whole flight envelope on the nonlinear component-level model of a turbofan engine, and the superiority over the Min-Max architecture is also validated.

Nonlinear component level model (NCLM) is a widely used model for aeroengines. However, it requires iterative calculation and is, therefore, time-consuming, which restricts its real-time application. This study aims at developing a simplified real-time modeling approach for turbofan engines. A mechanism modeling approach is proposed based on linear models to avoid the iterative calculation in NCLM so as to effectively reduce the computational complexity. Linear local models, of which the outputs are the solution of the balance equations in NCLM, are established at the ground operating points and are combined into a linear parameter varying (LPV) state-space model. Then, the model is extended throughout the full flight envelope in a polytopic expression and is integrated with the flow path calculation to obtain satisfactory real-time performance. In order to ensure the accuracy of the integrated model, the upper bound of convergence residual of the iteration is strictly set and consideration on the interpolation method is taken. The simulation results demonstrate that the integrated model requires much less computational resources than the NCLM does. Meanwhile, it maintains an acceptable accuracy performance and, therefore, is suitable for real-time application.

This paper presents a data-based Takagi-Sugeno (T−S) fuzzy modeling approach for aircraft engines in the flight envelope. We propose a series of T−S fuzzy models for engines with flight conditions as premises and engine linear dynamic models as consequences. By engine dynamic clustering, we determine rough T−S fuzzy models to approximate the nonlinear dynamics of engines in the flight envelope. After that, the maximum−minimum distance-based fuzzy c-means (MMD-FCM) algorithm comes to refine the fuzzy rules and the least square method (LSM) comes to identify premise parameters for each single rough model. The proposed MMD-FCM algorithm guarantees the refined results are stable and reasonable, and the identification improves the accuracy of the steady and transient phases. The model verification showed that the T−S fuzzy models for engines had a high accuracy with a steady error less than 5%, and that the root mean squared error (RMSE) of transient errors was less than 8 × 10−4 with good generalization ability in the flight envelope.

This paper proposes a novel architecture of limit protection including the references governors and limit governors and applies this architecture to limit protection in turbofan engines. References governors are designed as add-on schemes to a pre-stability engine control system that modifies reference commands to avoid constraints violation. Limit governors are proposed as an assistant part for references governors adjusting constraints to prevent references from stopping updates. The use of output admissible sets for a class of variable constraints is exploited to form invariant sets. Simulation results based on a turbofan engine model show that references governors with limit governors can effectively enforce the multiple constraints and provide enhanced engine thrust when steady violation occurs.

This paper deals with sensor faults of aircraft engines under uncertainties using a bank of second-order sliding mode observers (SMOs). In view of the effect of inevitable uncertainties on the fault reconstruction, a method combining H ∞ concepts and linear matrix inequalities (LMIs) is proposed, in which a scaling matrix is designed to minimize the gain of the transfer function matrix from uncertainty to reconstruction. However, robust design generally requires that engine outputs outnumber faults. In the case where the above-mentioned requirement is not satisfied, a bank of sliding mode observers is proposed to ensure the degrees of freedom available in robust design. In specific, each observer corresponds to a certain sensor with the hypothesis that the corresponding sensor will not have faults, to create one degree of design freedom for each observer. After fault occurrence, a large estimation error is expected in the observers with wrong hypothesis, and then a logic module is designed to detect sensor faults and obtain the optimal robust sensor fault reconstruction at the same time. The proposed approach is applied to a nonlinear engine component-level-model (CLM) simulation platform, and a numerical study is performed to validate the effectiveness.

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.

In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can’t be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative reduced kernel principal component analysis (IRKPCA) algorithm to extract fault features from original high-dimension observation without large additional calculation load and combine it with the HMM for engine gas-path fault diagnosis. The optimal kernel features are obtained by iterative sequential forward selection of the IRKPCA, and the features with lower dimensions are contracted through a trade-off between the fault information and modeling data scale in reduced kernel space. The similarity degree is designed to simplify the HMM modeling data using fault kernel features. Test results show that the proposed methodology brings a significant improvement in diagnostic confidence and computational efforts in the applications of a turbofan engine fault diagnosis during its steady and dynamic process.

Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear dynamic element in Wiener model are designed to tune engine operating conditions. Reduced-dimension matrix inversion incorporated with the fast leave one out cross validation strategy is utilized to decrease computational time for the selection of engine model feature parameters. An optimization algorithm is no longer needed compared to the former method. The contribution of this study is that a more convenient and appropriate methodology is developed to describe aircraft engine thermodynamic behavior during its static and dynamic operations. The methodology is evaluated in terms of computational efforts, dynamic and static estimation accuracy through a case study involving data that are generated by general aircraft engine simulation. The results confirm our viewpoints in this paper.

In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a “reconstruction signal” to estimate health parameters modeled as artificial inputs, and is not only applicable to long-time health degradation, but reacts much quicker in handling abrupt fault cases. In view of the inevitable uncertainties in engine dynamics and modeling, a weighting matrix is created to minimize such effect on estimation by using the linear matrix inequalities (LMI). A big step toward uncertainty modeling is taken compared with our previous SMO-based work, in that uncertainties are considered in a more practical form. Moreover, to avoid chattering in sliding modes, the super-twisting algorithm (STA) is employed in observer design. Various simulations are carried out, based on the comparisons between the KF-based scheme, the SMO-based scheme in our earlier research, and the proposed method. The results consistently demonstrate the capabilities and advantages of the proposed approach in health parameter estimation.

In this paper, a H ∞ /Leitmann approach to the robust tracking control design is presented for an uncertain dynamic system. This new method is developed in the following two steps. Firstly, a tracking dynamic system with simultaneous consideration of parameter uncertainty and noise is modeled based on a linear system and a reference model. Accordingly, a “nominal system” from the tracking system is defined and controlled by a H ∞ control to obtain the asymptotical stability and noise resistance. Secondly, by making use of a Lyapunov function and the norm boundedness, a new robust control with the “Leitmann approach” is designed to cope with the uncertainty. The two controls collaborate with each other to achieve “uniform tracking boundedness” and “uniform ultimate tracking boundedness”. The new approach is then applied to an aircraft turbofan control design, and the numerical simulation results show the prescribed performances of the closed-loop system and the advantage of the developed approach.

The study on the lateral movement of soil organic carbon (SOC) during soil erosion can improve the understanding of global carbon budget. Simulated rainfall experiments on small field plots were conducted to investigate the SOC lateral movement under different rainfall intensities and tillage practices. Two rainfall intensities (High intensity (HI) and Low intensity (LI)) and two tillage practices (No tillage (NT) and Conventional tillage (CT)) were maintained on three plots (2 m width × 5 m length): HI-NT, LI-NT and LI-CT. The rainfall lasted 60 minutes after the runoff generated, the sediment yield and runoff volume were measured and sampled at 6-min intervals. SOC concentration of sediment and runoff as well as the sediment particle size distribution were measured. The results showed that most of the eroded organic carbon (OC) was lost in form of sediment-bound organic carbon in all events. The amount of lost SOC in LI-NT event was 12.76 times greater than that in LI-CT event, whereas this measure in HI-NT event was 3.25 times greater than that in LI-NT event. These results suggest that conventional tillage as well as lower rainfall intensity can reduce the amount of lost SOC during short-term soil erosion. Meanwhile, the eroded sediment in all events was enriched in OC, and higher enrichment ratio of OC (ERoc) in sediment was observed in LI events than that in HI event, whereas similar ERoc curves were found in LI-CT and LI-NT events. Furthermore, significant correlations between ERoc and different size sediment particles were only observed in HI-NT event. This indicates that the enrichment of OC is dependent on the erosion process, and the specific enrichment mechanisms with respect to different erosion processes should be studied in future.

A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.

This paper proposes a novel min-max control scheme for aircraft engines, with the aim of transferring a set of regulated outputs between two set-points, while ensuring a set of auxiliary outputs remain within prescribed constraints. In view of this, an optimal augmented monotonic tracking controller (OAMTC) is proposed, by considering a linear plant with input integration, to enhance the ability of the control system to reject uncertainty in system parameters and ensure no crossing limits. The key idea is to use the eigenvalue and eigenvector placement method and genetic algorithms to shape the output responses. The approach is validated by numerical simulation. The results show that the designed OAMTC controller can achieve a satisfactory dynamic and steady performance and keep the auxiliary outputs within constraints in the transient regime.

The effects of water erosion (including long-term historical erosion and single erosion event) on soil properties and productivity in different farming systems were investigated. A typical sloping cropland with homogeneous soil properties was designed in 2009 and then protected from other external disturbances except natural water erosion. In 2012, this cropland was divided in three equally sized blocks. Three treatments were performed on these blocks with different simulated rainfall intensities and farming methods: (1) high rainfall intensity (1.5-1.7 mm min(-1)), no-tillage operation; (2) low rainfall intensity (0.5-0.7 mm min(-1)), no-tillage operation; and (3) low rainfall intensity, tillage operation. All of the blocks were divided in five equally sized subplots along the slope to characterize the three-year effects of historical erosion quantitatively. Redundancy analysis showed that the effects of long-term historical erosion significantly caused most of the variations in soil productivity in no-tillage and low rainfall erosion intensity systems. The intensities of the simulated rainfall did not exhibit significant effects on soil productivity in no-tillage systems. By contrast, different farming operations induced a statistical difference in soil productivity at the same single erosion intensity. Soil organic carbon (SOC) was the major limiting variable that influenced soil productivity. Most explanations of long-term historical erosion for the variation in soil productivity arose from its sharing with SOC. SOC, total nitrogen, and total phosphorus were found as the regressors of soil productivity because of tillage operation. In general, this study provided strong evidence that single erosion event could also impose significant constraints on soil productivity by integrating with tillage operation, although single erosion is not the dominant effect relative to the long-term historical erosion. Our study demonstrated that an effective management of organic carbon pool should be the preferred option to maintain soil productivity in subtropical red soil hilly region.

Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.