SkyMaintain: A Deterministic Regulatory-Aware AI Platform for Predictive Aircraft Maintenance

Abstract

Aircraft maintenance systems are increasingly challenged by growing system complexity, expanding data streams, and stringent regulatory oversight. While predictive maintenance methodologies have advanced through statistical learning and machine intelligence, existing solutions often lack integration with formal regulatory frameworks and structured explainability—both of which are essential in safety-critical aviation environments.

This paper presents SkyMaintain, a deterministic regulatory-aware predictive maintenance platform designed specifically for aviation systems. The platform integrates multivariate time-series modeling, probabilistic failure estimation, and anomaly detection within a layered architecture that embeds structured regulatory logic into the predictive inference pipeline. By intersecting machine learning outputs with encoded compliance constraints derived from aviation regulatory standards, SkyMaintain ensures that recommendations are both statistically informed and operationally permissible.

The system further incorporates a domain knowledge graph to enhance interpretability, a microservices-based implementation architecture for scalable deployment, and a zero-trust cybersecurity framework suitable for commercial, military, and executive fleet environments. A constrained optimization formulation is introduced to formalize the interaction between predictive intelligence and deterministic regulatory filtering.

Through this hybrid architecture, SkyMaintain advances predictive maintenance from a purely analytical exercise to a compliance-aware governance framework. The proposed approach contributes to the design of explainable, regulatorily aligned AI systems for aviation maintenance and establishes a foundation for future empirical validation in operational fleet environments.