PHM technology plays an increasingly significant role in modern aviation condition-based maintenance. As an important part of prognostics and health management (PHM), a health assessment can effectively estimate the health status of a system and provide support for maintenance decision making. However, in actual conditions, various uncertain factors will amplify assessment errors and cause large fluctuations in assessment results. In this paper, uncertain factors are incorporated into flight control system health assessment modeling. First, four uncertain factors of health assessment characteristic parameters are quantified and described by the extended λ-PDF method to acquire their probability distribution function. Secondly, a Monte Carlo simulation (MCS) is used to simulate a flight control system health assessment process with uncertain factors. Thirdly, the probability distribution of the output health index is solved by the maximum entropy principle. Finally, the proposed model was verified with actual flight data. The comparison between assessment results with and without uncertain factors shows that a health assessment conducted under uncertain conditions can reduce the impact of the uncertainty of outliers on the assessment results and make the assessment results more stable; therefore, the false alarm rate can be reduced.

A multi-echelon inventory allocation technique with maintenance ratio for civil aircraft spare parts was proposed in this paper. Multi-echelon inventory allocation model was established, which integrates maintenance rate into METRIC (Multi-echelon technique for recoverable item control) model, and then marginal analysis method was applied to solve this model and accomplish multi-echelon inventory allocation of spare parts. Firstly, the theory of multi-echelon inventory allocation model with maintenance ratio was elaborated, and the mathematical model was established with the fleet availability as the optimization objective, the cost and the safeguard probability as the constraints. Secondly, the procedure of solving multi-echelon inventory allocation model considering the influence of maintenance ratio is discussed based on the marginal analysis method. Thirdly, landing gear LRU (Line repairable units) of a typical civil aircraft were selected as the research object, multi-echelon inventory allocation was investigated by considering maintenance ratio. The analysis results show that the minimum safeguard cost and fleet availability are respectively 2759833 dollars and 0.9806, and the number of LRU of multi echelon inventory allocation are obtained under the condition of satisfying the constrained optimization. The feasibility and effectiveness of the developed method are verified by comparing the traditional analysis method. This paper provides guidance for the accurate allocation of multi echelon inventory optimization for maintenance resources on the premise of reasonable cost reduction.

At alpine regional airports, aircraft are covered with frozen ice when they encounter extreme weather such as heavy snow or frost. The movement parts of aircraft cabin doors, flaps and landing gear may be affected due to the infiltration of freezing ice, and the movement stagnation may occur when the the accumulation of ice is more serious. This paper sets up a mechanical performance test of frozen ice for this engineering problem to provide data that is beneficial to the selection of the mechanism drive and the determination of ice-breaking loads. The test is conducted based on the standard tensile shear test. In order to overcome problems such as the poor icing effect of the traditional specimen or the easy damage of the specimen ice, we improved the structure of the specimen and the method of the test. According to the characteristics of growth of frozen ice, we introduced freezing time, type of water quality and adhesion materials as test variables. The results show that: the ice adhesion strength of frozen ice increases and then decreases (−15 ${}^{\circ }$C∼−55 ${}^{\circ }$C). At the ambient temperature of −15 ${}^{\circ }$C∼−55 ${}^{\circ }$C and freezing for 2 h∼6 h, the ice adhesion strength of aluminum alloy surface ranges from 0.009 MPa to 0.568 MPa, and that of frozen ice on a silicone rubber surface is 0.005 MPa∼0.147 MPa. The duration of freezing did not significantly affect the adhesion strength of frozen ice. Among the three water qualities, the frozen ice from distilled water has the greatest adhesion strength, the lake water is the most medium, and the sea water is the smallest. The results of this test can be widely used in the determination of the ice-breaking load of civil aircraft, amphibious aircraft, ships, and the design of anti-ice/de-icing systems.

The reliability simulation of ultrasonic nondestructive testing (NDT) based on the COMSOL Multiphysics simulation software is used to research the influence factors of the angle between the sound beam axis and the crack orientation, and the crack burial depth. First, the COMSOL Multiphysics simulation models of ultrasonic NDT are established to obtain the ultrasonic detection amplitude of cracks with the same size at different angles or crack burial depths. According to different crack lengths, the simulation of ultrasonic detection amplitude under different angles or crack burial depths are carried out, and the probability of detection (POD) curve forms, such as Weibull and Lockheed function, refereed so as to determine the optimal POD curve form considering the influence of angle or crack burial depth. Finally, the minimum detectable crack size with 90% detection probability considering the influence of angle or crack burial depth can be inversed, thus a simulation method of ultrasonic NDT reliability based on COMSOL Multiphysics is formed. The simulation results show that the POD curves and minimum detectable crack size at different angles or crack burial depths in a specific structure are effectively obtained through the proposed method. It can provide some suggestion for the formulation of an ultrasonic testing scheme affected by angle and crack burial depth in engineering practice.

Turbine blisks are assembled using blades, disks and casings. They can endure complex loads at a high temperature, high pressure and high speed. The safe operation of assembled structures depends on the reliability of each component. Monte Carlo (MC) simulation is commonly used to analyze structural reliability, but this method needs to run thousands of computations. In order to assess the clearance reliability of assembled structures in an efficient and precise manner, the novel Kriging-based decomposed-coordinated (DC) (DCNK) approach is proposed by integrating the DC strategy, the Kriging model and the importance sampling-based Markov chain (MCIS) technique. In this method, the DC strategy is used to decompose a multi-objective problem into many single-objective problems. The relationships between these many single-objectives and the overall objective are then coordinated. The Kriging model is applied to establish the limit state functions of the single-objectives and multi-objective problems, while the MCIS method is used to assess the structural assembled clearance reliability. Moreover, a highly nonlinear complex compound function is first utilized to verify the DCNK model from a mathematical perspective. Then, the reliability of an aeroengine high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) is analyzed to validate the DCNK approach by considering thermo-structural interaction. The analytical results show that the reliability is 0.9976 when the allowance value of the BTTRC is 1.7650 × 10−3 m. Compared with different methodologies (including direct simulation, the classical Kriging model, and the weighted response surface method (WRSM)), the proposed method holds obvious advantages in computing time and precision, as well as simulation efficiency and precision. The efforts of this paper provide a useful approach to analyzing assembled clearance reliability and contribute to the development of structural reliability theory.

The reliability analysis of complex mechanisms involves time-varying, high-nonlinearity, and multiparameters. The traditional way is to employ Monte Carlo (MC) simulation to achieve the reliability level, but this method consumes too much computing resources and is even computationally intractable. To improve the efficiency and accuracy of dynamic probabilistic analysis of complex mechanisms, an intelligent extremum surrogate modeling framework (IESMF, short for) is proposed based on extremum response surface method (ERSM), combined with artificial neural network (ANN) method and an improved optimize particle swarm optimization (PSO) method. Hereinto, the ERSM is used to simplify the dynamic process of output response to the extremum value of transient analysis; ANN is applied to establish a mathematical model between input variables and response, and the improved PSO method is utilized in search of initial weights and thresholds of the model. The effectiveness of the IESMF is demonstrated to perform the Rack-and-pinion steering mechanism (RPSM) reliability analysis. The results show that when the allowable value of gear root stress is equal to 850 MPa, the RPSM has a reliability degree of 0.9971. Through the validation process, it is illustrated that IESMF is accurate and efficient in dynamic probabilistic analysis of complex mechanisms, and its comprehensive performance is better than the MC method and ERSM. The research effort offers new ideas for the reliability estimation of a complex mechanism, thus enriching the method and theory of mechanical reliability design.