Performance degradation assessment for aircraft environmental control system: A method based on visual cognition.

Area of Science:

• Aerospace Engineering
• Systems Engineering
• Signal Processing

Background:

• Aircraft environmental control systems (AECS) are critical for flight safety.
• Performance degradation can lead to system failure and severe consequences.
• Effective degradation assessment is essential for timely maintenance.

Purpose of the Study:

• To propose an integrated framework for assessing AECS performance degradation.
• To introduce visual cognition theory into the degradation assessment method.
• To develop a real-time health indicator for AECS.

Main Methods:

• Observer-based residual error generation to capture degradation information.
• Transformation of residual error into a 2D image using a permutation method.
• Nonsubsampled contourlet transform (NCST) for multiscale, multidirectional feature extraction.
• Diffusion maps (DM) to establish manifold space for health degradation analysis.
• Geodesic distance calculation to derive a confidence value (CV) as a health indicator.

Main Results:

• The proposed method effectively reflects the degradation process of the AECS.
• Simulation data from a MATLAB/Simulink model with introduced faults validated the approach.
• The developed confidence value (CV) accurately represents the degradation trend.

Conclusions:

• The integrated framework provides an effective approach for AECS performance degradation assessment.
• The confidence value (CV) serves as a reliable health indicator for real-time monitoring.
• The method enhances predictive maintenance capabilities for critical aircraft systems.