Embedded DPHM Modules for Humanoid Robot Health Management

The patent applies preliminary action by embedding DPHM modules during the initial system design and commissioning phase rather than attempting post-commissioning integration. The modules are integrated into the controller architecture before the robotic system is deployed, making DPHM functionality an inherent part of the system from the outset. This approach avoids the impracticability of adding such functionality later while managing system complexity through planned integration.

The patent segments the DPHM functionality into separate, modular modules that can be independently integrated into different control levels of the robotic system. Each DPHM module handles specific diagnostic, prognostic, or health management tasks, allowing the system to scale DPHM capabilities without overwhelming system complexity. This modular approach enables selective integration at appropriate system levels.

The patent implements universality by designing DPHM modules with standardized interfaces and functionalities that can be applied across multiple control levels and different robotic system configurations. The modules are built on common architectural principles and data structures, allowing them to serve multiple purposes and be reused throughout the system hierarchy. This multi-functionality reduces development costs by avoiding redundant implementation efforts at each control level.

The patent reduces system development costs through preliminary action by establishing standardized DPHM module architectures and interfaces during the initial design phase. By defining common data structures, communication protocols, and integration patterns upfront, the system avoids costly retrofits and interface developments later. The preliminary framework enables scalable deployment across multiple control levels without proportionally increasing development expenses.

The patent applies parameter changes by transitioning from simple static bit representations to structured data structures that capture continuous health parameters and their trends. Instead of merely storing binary fault states, the system records quantitative measurements such as sensor readings, performance metrics, and degradation indicators. This parameter enrichment enables advanced fault detection methods that go beyond simple threshold comparisons, including trend analysis and predictive modeling.

The patent introduces intermediary data structures and processing layers between the raw sensor data and the fault detection logic. These intermediaries include structured health data formats, normalization layers, and feature extraction mechanisms that transform raw measurements into meaningful diagnostic information. This intermediary layer preserves data simplicity while enabling sophisticated fault detection capabilities through structured data organization and processing.