Flight-State-Driven Threshold Optimization Framework for Rotorcraft HUMS

초록

Conventional thresholding methods for rotorcraft Health and Usage Monitoring Systems (HUMS) often neglect flight-condition variability, resulting in frequent false alarms. To address this, a flight-state-driven threshold optimization framework that explicitly incorporates flight parameters and operational context is proposed. The proposed method combines proactive spike filtering with Principal Component Analysis (PCA) of flight parameters to distinguish flight-state-driven Condition Indicator (CI) variations from spike-like artifacts, and then re-estimates thresholds from the filtered CI distribution. The framework is evaluated using HUMS data collected from in-service rotorcraft, focusing on vibration- and fatigue-sensitive transmission components. Quantitative results show that the framework significantly reduces the Background Alarm Rate (BAR) to approximately 0.030 compared to the baseline of 0.202, while maintaining a high In-window Alarm Concentration (IAC) comparable to conventional methods. These validation results using real fault cases confirm the practical applicability of the approach to operational rotorcraft environments, indicating that the framework effectively reduces unnecessary alarms and enhances the stability and reliability of fault detection compared with conventional methods. The proposed framework offers an explainable, consistent, and operationally grounded basis for periodic threshold reviews in HUMS. It complements existing practices in Condition-Based Maintenance (CBM), providing a practical pathway to enhance confidence in vibration-based diagnostics under diverse flight conditions.

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