Annotation

Annotation: The article presents a systematic review of modern diagnostic methods for robotic systems (RTCs). The key domestic and international standards that ensure the unity of requirements for functional safety, compatibility of data exchange architectures and reliability management are considered as a normative and terminological basis. Specialized diagnostic methods of the main RTC subsystems are presented, systematized by node types. For mechanical components, vibroacoustic approaches are shown, including envelope analysis, ordinal methods, and time-frequency analysis used in non-stationary operating conditions. Methods of parametric degradation monitoring and model-based diagnostics are shown for the electrical and electronic parts. Mode-invariant methods using deep learning, such as convolutional neural networks for analyzing time-frequency representations of signals, are described for drives and actuators. Special attention is paid to the role of digital twins in failure modeling, synthetic data generation, and residual resource forecasting. The central place in the review is occupied by approaches to complex diagnostics at the level of the entire RTK. Architectural principles and modern intelligent technologies for integrating heterogeneous data are considered, such as probabilistic models for merging information in conditions of uncertainty, ontologies for semantic integration of knowledge and ensuring traceability of solutions, as well as hybrid neuro-symbolic architectures combining the power of pattern recognition with logical interpretability. The necessity of transition from isolated solutions to integrated diagnostic systems capable of supporting functional safety and adaptive management in conditions of partial failures is formulated. The key conclusion is the requirement for the development of standardized verification protocols providing for testing on independent modes and equipment instances, and the creation of a semantic-cognitive control loop combining diagnostics, forecasting, and planning for next-generation autonomous robotic systems.