Mujia Shi

Machine learning is increasingly used for spacecraft telemetry monitoring, fault diagnosis, health assessment, mission planning, visual navigation, and execution review. In space missions, explainability is a foundation of trustworthy artificial intelligence because engineers must understand the evidence behind model outputs before incorporating them into operational decisions. Explainability should therefore transform internal model computations into evidence that engineers can inspect during mission execution and integrate into an intuitive mental model of spacecraft state, model rationale, and possible action consequences. Such evidence may include channel–time patterns, rule paths, prototype events, active constraints, or subsystem propagation hypotheses. It must remain traceable to the model, consistent with spacecraft modes and physical constraints, and usable in reviewable decisions. This review distinguishes the mechanism that generates an explanation from the strength of its spacecraft validation. It examines interpretable-by-design models and six post-hoc families, separating direct spacecraft evidence from methods transferable from adjacent domains. A V0–V5 scale describes evidence ranging from generic experiments to measured mission-support benefit. Finally, the review discusses the Open-Box method and examines how exact and consistent regional representations may be relevant to piecewise-linear network families within verified operating regions.

The operational reliability of the elastic wheel, essential for specialized vehicle mobility on complex terrain, is critically constrained by fatigue failure under multi-axis ground loads. While high-fidelity physics-based simulation provides an accurate assessment, its “one-simulation-per-test” paradigm is inefficient for exploring multi-condition, multi-parameter designs. Conversely, purely data-driven methods are hindered by the scarcity of high-quality fatigue data. This paper proposes LightGBM-CH, an integrated framework that couples Discrete Element Method–Multi-Body Dynamics (DEM-MBD) simulation with an enhanced LightGBM model to overcome these limitations. The framework first converts high-fidelity simulations into a configurable data generator, producing batches of dynamic load–stress response data. A physics-informed feature engineering scheme then extracts 122 discriminative features characterizing six-dimensional loads, fatigue damage metrics, and load–stress coupling. To address the “small-sample, high-dimensional” challenge, a tailored training strategy incorporating robust scaling, correlation-based feature selection, and stability-constrained hyperparameter optimization is developed. Simulation experiments demonstrate that the LightGBM-CH model achieves a determination coefficient of 0.9251 and a root mean square error of 67.06, significantly outperforming benchmark models in accuracy and generalization. The study validates the framework’s engineering efficacy, identifies key influencing factors such as peak–stress ratio, and provides an intelligent, data-informed pathway for fatigue-resistant elastic wheel design.

In current evaluations of intelligent system capabilities, there is a problem with the difficulty of unifying quantitative scoring results with qualitative grading standards, which severely limits cross-platform and cross-task intelligent comparisons and grading certifications. To address this issue, this paper proposes a hierarchical parameter partitioning equivalent mapping method (HPP-EM) to construct a mapping mechanism from quantitative scores to qualitative grades. The method comprises three modules: Nonlinear Interval Partitioning (NIP) constructs intelligent capability intervals based on the principle of diminishing capability margins; Quantification-to-Grade Mapping (QGM) introduces a set of criteria to determine mapping rules; and Dynamic Threshold Generation (DTA) calculates the lower score thresholds and total score thresholds for each grade based on indicator weights. The model is interpretable and supports mapping across multiple metrics and multi-level systems. Experimental validation was conducted using evaluation data from a deep-space unmanned system, demonstrating that HPP-EM accurately reflects system intelligence levels and exhibits good adaptability and scalability. This paper provides a traceable, structured mapping scheme for unified intelligence level assessment.