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Related Experiment Video

Updated: Apr 28, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

An optimization method for condition based maintenance of aircraft fleet considering prognostics uncertainty.

Qiang Feng¹, Yiran Chen¹, Bo Sun¹

¹School of Reliability and Systems Engineering, Beihang University, Beijing, China.

Thescientificworldjournal

|June 4, 2014

Summary

This study proposes an optimized condition based maintenance (CBM) strategy for aircraft fleets, reducing costs and dispatch risks by considering prognostics uncertainty. An improved genetic algorithm optimizes fleet maintenance and dispatch for mission success.

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Last Updated: Apr 28, 2026

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Published on: November 3, 2023

Area of Science:

Aerospace Engineering
Operations Research
Reliability Engineering

Background:

Condition Based Maintenance (CBM) is crucial for aircraft fleet management.
Prognostics uncertainty in Remaining Useful Life (RUL) complicates CBM optimization.
Balancing maintenance costs and dispatch risks is a key challenge.

Purpose of the Study:

To develop an optimization method for aircraft fleet CBM considering prognostics uncertainty.
To minimize maintenance costs and dispatch risks for fleet operations.
To enhance mission success through optimized CBM and dispatch strategies.

Main Methods:

Analysis of CBM and dispatch processes, defining single aircraft strategy sets.
Transformation of RUL distribution into aircraft failure probability and fleet health status matrix.
Development of a cost and risk calculation method based on health and maintenance matrices.
Application of an improved genetic algorithm for fleet dispatch and CBM optimization under acceptable risk.

Main Results:

The proposed method translates fleet CBM optimization into a combinatorial optimization problem of single aircraft strategies.
A fleet health status matrix and a cost/risk calculation method were established.
The improved genetic algorithm effectively optimized fleet dispatch and CBM.
A case study with 10 aircraft validated the method's effectiveness.

Conclusions:

The developed method enables optimized and controlled aircraft fleet operations oriented towards mission success.
Considering prognostics uncertainty is vital for effective CBM in aircraft fleets.
The improved genetic algorithm provides a robust solution for complex fleet management problems.