DSFB Structural Semiotics Engine for Robotics Health Monitoring: A Deterministic Augmentation Layer for Typed Residual Interpretation of Joint Degradation, Actuator Drift, and Kinematic Anomalies in Safety-Critical Robotic Systems

Abstract

Robotic systems in manufacturing, collaborative assembly, surgical assistance, and autonomousinspection already produce dense residual streams through controller error signals, torque estima-tion discrepancies, vibration monitoring baselines, and kinematic model deviations. Operationalaction, however, remains dominated by scalar threshold alarms that suppress temporal struc-ture. This paper studies the DSFB Structural Semiotics Engine as a deterministic augmentationlayer over those existing residual streams in robotic health monitoring.It does not propose a replacement controller, a new fault detection architecture, or a com-peting prognostics framework. Instead, it maps residual trajectories into explicit objects —residual sign, admissibility envelope, grammar state, and provenance-aware motif entries — sothat slow actuator drift, bearing degradation onset, kinematic chain loosening, and structuralfatigue precursors can be represented in a typed and inspectable form.The paper makes a bounded claim. It shows how deterministic intermediate representationscan support auditability arguments and operator review under ISO 10218-1:2025, ISO 10218-2:2025, IEC 61508, and ISO 13849, and how DSFB formal objects can be instantiated usingrobotic observables such as joint torque residuals, vibration envelopes, position tracking errors,and current draw anomalies. It does not prove standards compliance, completed qualification,universal superiority over existing PHM/FDD baselines, or physical root-cause attribution frompublic data alone.The empirical evidence presented is Stage III public-data evidence on twenty real-worldrobotics benchmarks across three residual-source families. All twenty datasets arephysical-hardware recordings under permissive licences (Apache-2.0 / MIT / CC-BY-4.0 / CC-BY-SA-4.0 / BSD-3-Clause / academic-fair-use). Zero synthetic or simulated data is admitted.Under the fixed read-only protocol, DSFB is evaluated strictly as a downstream observer layerover residuals already produced by existing monitoring infrastructureDSFB does not compete with existing robot health monitoring, fault detec-tion, or prognostics systems — it augments them. Those systems continue tooperate unchanged. DSFB reads the residual streams they already produce and returnsa typed, deterministic, human-readable interpretation of what the residuals mean struc-turally. The upstream PID controllers, model-predictive controllers, joint torque estima-tors, and vibration analyzers are not modified, replaced, or disabled. If DSFB is removed,upstream behavior is unchanged.Claims Not MadeThis paper does not claim:• that the semiotic approach subsumes existing PHM, FDD, RUL estimation, or ML-based fault classification in all robotic regimes;• that the Stage III public-data demonstration constitutes a complete empirical validationfor all robot morphologies, payloads, or operating environments;• that the current evidence supports physical root-cause identification of specific mechan-ical failure mechanisms;• that admissibility envelopes derived from healthy-window statistics are universally op-timal or automatically calibrated;• that this manuscript establishes ISO 10218, ISO 13849, or IEC 61508 compliance,completed qualification, or deployment readiness;• or that the heuristics bank, in its current form, exhausts the interpretive possibilitiesof real robotic operating environments