# MOSOF with NDCI: A Cross-Subsystem Evaluation of an Aircraft for an Airline Case Scenario

## TL;DR

This paper introduces a new framework combining NDCI and MOSOF to optimize sensor placement in aircraft systems, improving diagnostic accuracy while balancing cost and reliability.

## Contribution

The novel integration of NDCI with MOSOF enables stakeholder-aware sensor optimization in complex systems like aircraft.

## Key findings

- NDCI outperforms mRMR in diagnostic accuracy for engine and ECS subsystems.
- A Pareto-optimal sensor suite of 12–14 sensors balances performance, cost, and reliability for airline use-cases.
- The NDCI-MOSOF workflow offers a transparent pathway for sensor design decisions in aircraft health management.

## Abstract

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed.