Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine

Aero-engine gas path health monitoring plays a critical role in Engine Health Management(EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine(HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First,to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then,a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope.

Gossypol: phytoalexin of cotton

Sesquiterpenoids are a class of 15-carbon secondary metabolites that play diverse roles in plant adaptation to environment.Cotton plants accumulate a large amount of sesquiterpene aldehydes(including gossypol)as phytoalexins against pathogens and herbivores.They are stored in pigment glands of aerial organs and in epidermal layers of roots.Several enzymes of gossypol biosynthesis pathway have been characterized,including 3-hydroxy-3-methylglutaryl coenzyme A reductase(HMGR)and farnesyl diphosphate synthase(FPS)that catalyze the formation of the precursor farnesyl diphosphate(FPP),(+)-δ-cadinene synthase(CDN)which is the first enzyme committed to gossypol biosynthesis,and the downstream enzymes of CYP706B1 and methyltransferase.Expressions of these genes are tightly regulated during cotton plants development and induced by jasmonate and fungi elicitors.The transcription factor Ga WRKY1 has been shown to be involved in gossypol pathway regulation.Recent development of new genomic platforms and methods and releases of diploid and tetraploid cotton genome sequences will greatly facilitate the elucidation of gossypol biosynthetic pathway and its regulation.

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.

Robust decentralized control design for aircraft engines: A fractional type

A new decentralized control for aircraft engines is proposed. In the proposed control approach, aircraft engines are considered as uncertain large-scale systems composed of interconnected uncertain subsystems. For each subsystem, the time-varying uncertainty, including parameter disturbances and interconnections in/between subsystems, is depicted by a class of general nonlinear functions. A fractional robust decentralized control with two parts, the nominal one and the fractional one, is presented. The nominal control guarantees the asymptotical stability of the engine system without uncertainty. The fractional part aims at overcoming the influences of uncertainty. Compared to the previous studies, the presented control provides not only an extra flexibility for the system performance tuning by the fraction-type gain but also a facility for the control input calculation. The proposed control approach is applied to a turbofan engine with two subsystems. The computer simulation shows that, in the flight envelope, the fractional control not only guarantees the closed-loop system uniform boundedness and ultimate uniform boundedness but also shows good economy.

Direct thrust control for multivariable turbofan approach

A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to shaft speed command and pressure ratio command,the thrust will be directly controlled by an optimal controller with two control variables.LPV model of the engine is established for the designing of thrust estimator and controller.A robust LPV H∞filter is introduced to estimate the unmeasurable thrust according to measurable engine states.The thrust estimation error system is proved to be Affinely Quadratically Stable(AQS)in the whole parameter box with a prescribed H∞performance indexγ.Due to the existence of overdetermined equations,the solving of controller parameters is a multi-solution problem.Therefore,Particle Swarm Optimization(PSO)algorithm is used to optimize the controller parameters to obtain satisfactory control performance based on the engine’s LPV model.Numerical simulations show that the thrust estimator can acquire smooth and accurate estimating results when sensor noise exists.The optimal controller can receive desired control performance both in steady and transition control tasks within the engine working states above the idle,verifying the effectiveness of the proposed DTC architecture’s application in thrust direct control problem.

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed control system includes four parts,namely a predictive model,rolling optimization,online correction,and feedback correction.Considering the strong nonlinearity of engine,a predictive model is established by Back Propagation(BP)neural network for the entire flight envelope,whose input and output are determined with random forest algorithm and actual situation analysis.Rolling optimization typically uses SQP as the optimization algorithm,but SQP algorithm is easy to trap into local optimization.Therefore,the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP.In addition to the traditional three parts of model predictive control,an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain.Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy,and the proposed control system can achieve good tracking performance with the limited parameters within the safe range。

Limit protection design in turbofan engine acceleration control based on scheduling command governor

A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities(LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying(LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set(MOAS).The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition,the design of contractive sets makes conservativeness tunable.