PROJECT TITLE :

A Non-Probabilistic Metric Derived From Condition Information for Operational Reliability Assessment of Aero-Engines

ABSTRACT:

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 part is of great significance for improving flight safety. Traditionally, reliability assessment is meted out by statistical analysis on massive failure samples. Because the operational reliability of a particular aero-engine is a personal downside lacking statistical sample knowledge, traditional reliability assessment methods may be insufficient to assess the operational reliability of a private aero-engine. The operational states of the aero-engine can be identified by its condition info. Changes within the condition info mirror 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 data from the fault-free state, and the current state. A condition subspace is first obtained by kernel principal element analysis (KPCA). Subspace similarity is then represented by subspace angles, i.e., kernel principal angles (KPAs). The cosine perform is finally utilised as a mapping perform 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 info is needed for computation of the similarity index, thus it will be performed conveniently for online assessment. The effectiveness of the proposed technique is validated by 3 case studies regarding the health assessment of aero-engines subjected to system-level and element-level degradation. The positive results demonstrate that the proposed SI is an effective metric for operational reliability assessment of individual aero-engines.


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