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Purpose This study aims to propose an audit-safe analytics architecture to integrate predictive maintenance decision support into regulated aircraft maintenance environments while preserving deterministic rule authority, regulatory compliance and human accountability. Design/methodology/approach A layered system architecture was developed that separates probabilistic analytics from deterministic rule engines derived from approved maintenance data. Governance controls, including advisory containment, traceability, version control and human-in-the-loop oversight, were embedded at the architectural level. A structured validation protocol evaluated rule primacy enforcement, audit reconstruction capability and advisory containment across representative maintenance scenarios. Findings The results demonstrate that predictive analytics can enhance situational awareness and planning without transferring decision-making authority from certified personnel. Structural separation between advisory models and compliance logic mitigates automation overreach and preserves audit defensibility. Practical implications This framework provides a governance-aligned blueprint for deploying analytics within Part 145 and airline maintenance organizations without compromising continuing airworthiness or safety management system obligations. Originality/value This study reframes predictive maintenance integration as an architectural governance problem rather than an algorithmic optimization challenge, offering a defensible pathway for analytics adoption in safety-critical aviation contexts.
Moses Olushola Onilede (Wed,) studied this question.