A non-probabilistic metric derived from condition information for operational reliability assessment of aero-engines

The aero-engine is the heart of an airplane. Operational reliability assessment that aims to identify the reliability level of the aero-engine in the service phase is of great significance for improving flight safety. Traditionally, reliability assessment is carried out by statistical analysis on large failure samples. Because the operational reliability of a specific aero-engine is an individual problem lacking statistical sample data, traditional reliability assessment methods may be insufficient to assess the operational reliability of an individual aero-engine. The operational states of the aero-engine can be identified by its condition information. Changes in the condition information reflect the performance degradation of the aero-engine. Aiming at the assessment of the operational reliability of individual aero-engines, a novel similarity index (SI) is proposed by analyzing the condition information from the fault-free state, and the current state. A condition subspace is first obtained by kernel principal component analysis (KPCA). Subspace similarity is then represented by subspace angles, i.e., kernel principal angles (KPAs). The cosine function is finally utilized as a mapping function to transform the subspace angles into a similarity index. The index can be used as a non-probabilistic metric for operational reliability assessment. Only the condition information is needed for computation of the similarity index, thus it can be performed conveniently for online assessment. The effectiveness of the proposed method is validated by three case studies regarding the health assessment of aero-engines subjected to system-level and component-level degradation. The positive results demonstrate that the proposed SI is an effective metric for operational reliability assessment of individual aero-engines.