Chapter 7: Health Management and Safety Assessment¶
Intelligent unmanned systems belong to typical complex systems, where "complex systems" generally refer to those with large scale, complex structure, diverse functions, varied fault modes, and unknown or dynamically changing external environments. Unmanned systems typically exhibit characteristics such as nonlinearity, dynamic variability, large scale, hierarchical structure, and decentralization. As system complexity increases, the number and probability of component failures also rise significantly. Therefore, reducing failure probability and mitigating the consequences of failures are central concerns in system health and safety assessment.
7.1 Background and Theory¶
Health management and safety assessment are critical for intelligent unmanned systems. Health management emphasizes real-time monitoring of system status and predictive maintenance, whereas safety assessment focuses on risk identification, probability calculation, and hazard mitigation.
7.1.1 Safety Requirements for Unmanned Systems¶
Safety requirements vary significantly across different types of unmanned systems: Embodied intelligent systems (e.g., quadruped robots) operate indoors at low speeds, thus having relatively lower safety requirements; autonomous vehicles operate at high speeds on roads, demanding extremely high safety standards; drones operate in complex three-dimensional meteorological environments and face dual threats of "potential energy + kinetic energy," making low-altitude safety a key bottleneck constraining industry development.
7.1.2 Core Value of Health Management and Safety Assessment¶
Constructing a systematic and standardized framework for health management and safety assessment is not only an essential technical pathway to ensure stable and reliable operation of unmanned systems, but also a core strategic initiative for building a trustworthy intelligent system framework for the future—enabling unmanned systems to transition from “functional” to “trustworthy.”
7.2 Framework and Interfaces¶
RflySim provides a comprehensive support system for health management and safety assessment, enabling rapid testing under extreme conditions, a closed-loop evaluation framework integrating hardware-in-the-loop and software-in-the-loop, real-time state monitoring with indicator visualization, data-driven predictive health management, and high-fidelity “simulation-to-reality” transfer capabilities.
7.2.1 Fault Modeling and Injection Framework¶
The fault modeling and injection framework supports multi-layer fault injection across model, firmware, environment, communication, intelligent algorithm, and swarm task layers. It accommodates typical fault scenarios—including sensor failures, motor malfunctions, and communication interruptions—enabling multi-dimensional, multi-level validation of system robustness.
7.2.2 Evaluation and Diagnosis Algorithm Development Framework¶
Leveraging dual-channel interfaces for Simulink/DLL models and flight controllers, this framework enables structured test case design and automatic triggering mechanisms, supporting a unified evaluation workflow across simulation and real-world phases. Through flight controller log parsing and safety metric computation, it facilitates multi-dimensional performance analysis—including mission success rate, fault recovery capability, and more.
7.3 Showcase of Representative Cases¶
Fault Injection Experiment:
Fault Injection and Diagnosis Algorithm Validation:
7.4 Course-Linked Video Lectures¶
Public Lecture Replay (Session 6: Health Management and Societal Safety Assessment):
7.5 Chapter Experiment Cases¶
The relevant verification experiments and guided cases for this chapter are stored in the [Installation Directory]\RflySimAPIs\7.RflySimPHM folder.
7.5.1 Interface Learning Experiments¶
Stored in the 7.RflySimPHM\0.ApiExps folder, these experiments cover foundational platform interface tutorials and general introductions to various tools.