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Designing an audit-safe analytics architecture for aircraft maintenance decision support in regulated aviation environments.

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Bibliographic Details
Title:	Designing an audit-safe analytics architecture for aircraft maintenance decision support in regulated aviation environments.
Authors:	Onilede, Moses Olushola¹ (AUTHOR) monilede@encycloamts.com
Source:	Aircraft Engineering & Aerospace Technology. 2026, Vol. 98 Issue 6/7, p876-886. 11p.
Subjects:	Decision support systems, Rule-based programming, Airplane maintenance, Condition-based maintenance, Aviation law, Safety, Supervisory control systems
Abstract:	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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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. [ABSTRACT FROM AUTHOR]
ISSN:	17488842