Cargo airdrop has long been one of the most important measures to deal with urgent immediate needs, such as providing assists in military operations and sending relief to disaster areas, just to name a few. Because it is carried out during flight, it is necessary to investigate the influences of the drop process on flight characteristics to ensure successful execution of the task. This article mainly studies the modeling of flight systems in large flying transport planes with cargo moving in it. By building up rational frame definition, the complete nonlinear force and moment equations are established by using the Newton's second law and the Euler angle relation of rigid body. Then the effects of moving process parameters including cargo mass and moving items upon the flight are analyzed. Computerized simulation of the whole process shows that the cargo moving would exert enormous effects upon the flight characteristics and quality of controlling; therefore, a special control strategy should be developed to guarantee the required system performances.

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system.

During airdrop of heavy load, the flight parameters vary continuously as the load moves in the hold, and change suddenly when the load drops out. This process deteriorates the flight quality and control characteristic as the load becomes heavier. Based on the simplified airdrop flight equations, the backstepping and switch control methods are developed to tackle the flight state holding and disturbance/uncertainty (such as large-scale flight condition, pilot manipulation error, system measure delay, etc.) attenuation problem in this paper. Moreover, these methods can be used as a reference for pilot manipulating during airdrop. With the backstepping theory, an adaptive controller is synthesized for the purpose of stabilizing the transport when the load moves in the hold, and then a coordinated switch control method is used to control the aircraft when the condition jumps from the existence of load at the rear of fuselage to no load in the fuselage. Simulation results show that the proposed controllers not only provide effective state holding during airdrop, but also achieve robust performance within wide flight conditions.

Failure prognosis is the key point of prognostic and health management or condition-based maintenance, the multiple uncertainty sources in real world will lead to inaccurate prediction. In this paper, an advanced failure prognosis method with Kalman filter is presented to address the real-world uncertainties. The multiple uncertainty sources are analyzed and classified first and then theoretical methods are derived, respectively, for the different uncertainty sources. Afterward, the failure prognosis algorithm is developed by taking into consideration. In the end, an aircraft fuel feeding system health monitoring case simulation is presented to demonstrate the effectiveness of the proposed method.

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.

Airdrop is the most important approach for crisis transaction and unexpected events, it is necessary to investigate the flight characteristics of transport aircraft during the dropping process. This paper mainly focuses on the stability, controllability and model simplification of large aircraft with heavy cargo airdrop. In this process, the primary elements which have impact on force and moment are studied theoretically, the role of cargo mass, moving parameters and other factors on dynamical characteristics have been assessed by simulation and analysis. And then the aircraft model simplification is completed for control system designing in future. All the work above shows that the parameters of cargo moving play a dominant role in flight characteristics and the flight equations can be simplified to reduce the design complexity.