The air braking system is crucial for the safe operation of high-speed trains but is susceptible to faults from harsh environments and prolonged use. However, faulty data in practice are still rare because of the "safety oriented principle". For this purpose, fault injection is regularly employed. Due to the stochastic nature of faults in the system, random fault injection can more realistically simulate faulty scenarios compared to the deterministic fault injection. The traditional method entails analyzing a significant amount of raw data to extract the fault distribution function, followed by random sampling. However, the obstacles lie in the scarcity of raw fault data and the labor-intensive nature of constructing the fault distribution function. This paper proposes a layered random fault injection method based on multiple Markov chains. First, a multi-layer structured fault model base is established for the system, followed by the implementation of layered fault injection. Then, the random fault types and degrees are realized using Markov chains, in which the fault probability function is determined by the state transition matrix. Subsequently, a low-complexity Alias sampling algorithm is proposed for discrete random sampling. The nominal model is transformed into a corresponding fault model based on the sampling outcomes, facilitating the acquisition of fault data. Finally, a graphical user interface is developed to present and visualize the validation results.
Most existing risk prediction methods focus on constructing risk element sets and analyzing their uncertainties but do not deeply explore the correlation types and intensities of factors, resulting in large errors in the comprehensive risk prediction results. In this paper, a new integrated risk prediction method is proposed based on the correlation types of tasks in defense task planning and execution. The approach mainly includes the following steps: First, based on the difference of the sequence and mode of action of link tasks, three correlation types (hierarchical, synergistic, and independent) are defined among them, and various correlation measurement techniques are proposed to model these abstract correlation relations and provide data basis for constructing risk decision graphs. Secondly, the rotation extraction strategy is introduced to excavate the internal correlation law between link tasks and generate their hierarchical topology to ensure the rational distribution of their hierarchy positions in defense missions. Then, the intra-layer risk weight is determined based on the centrality of each node in the topology structure, and then the comprehensive risk prediction weighting graph is constructed. Finally, the path analysis is used to assess the rationality of the hierarchical topology structure of the link tasks, and the validity of the proposed method is verified using the test sample set. The results show that compared with other approaches, the predicted results of the proposed method more closely approximate the actual outcomes.
In this paper, a health status assessment scheme is studied for the attitude control system (ACS) of fixed-wing unmanned aerial vehicle (FWUAV) based on an improved multivariate state estimation technology that incorporates a dynamic memory matrix. Firstly, the parameters of the FWUAV representing the health status of the ACS are selected as feature parameters, and the historical health data of the feature parameters for the FWUAV is compared with the real-time test data. At the same time, the multivariate state estimation technology is applied to obtain the abnormal degree of components for the ACS. Based on the analytic hierarchy process and the expert experience, the weights are obtained for the different elements at the same functional level of the ACS. Secondly, the functional structure of the ACS is analyzed, and the abnormal degree is calculated for the ACS by combining the concept of reconfigurability and the weight information of each element, and the health status assessment indicators are further established. The health status result for the ACS of the FWUAV is acquired according to the efficiency value of the indicators and the abnormal degree of the ACS. Finally, the effectiveness of the developed algorithm is verified by the simulation analysis.
Solid-state light detection and ranging (LiDAR) has the advantages of low cost, small size and strong practicability. However, it faces challenges in simultaneous localization and mapping applications due to its small field of view and irregular scanning patterns. A solid-state LiDAR simultaneous localization and mapping system containing intensity information is proposed. In order to solve the irregular scanning characteristics of solid-state LiDAR, we introduce a data preprocessing framework and add intensity feature points to the front-end odometer. This improves the accuracy and robustness of positioning when geometric feature points are scarce, thus solving the problem of feature point degradation caused by a finite field of view. In the back-end optimization stage, we combine the geometric feature residuals with the intensity feature residuals through the proposed consistent difference function, so that the system can maintain good performance even in challenging environments. Finally, we conducted an extensive evaluation of the proposed algorithm on official datasets and various datasets collected from multiple platforms, and the results confirmed the validity of our approach. Compared with the corresponding method, in indoor scenes, the absolute trajectory error and relative attitude error are decreased by 54.5% and 5.3%. In outdoor scenes, the absolute trajectory error and relative attitude error are decreased by 29.6% and 58.8%.
In this paper, asymptotical synchronization in mean square,