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Abstract

Rotating turbine engine disk is one of the most critical components of the aircraft engine. Therefore, fault detection for rotating turbine engine disk is particularly significant. An adaptive Gaussian mixture model is put forward to deal with the dynamic working process of rotating turbine engine disk. Different from the standard expectation–maximization algorithm, the criterion based on minimum within-cluster distance and maximum between-cluster distance is used to update the weight of each Gaussian component. The incoming samples obtained by a forgetting mechanism are adopted to modify the fault detection model by exponentially weighted algorithm, which improves the ability of fault detection model to accommodate the actual situation of turbine engine disk. The comparison results between the multiple principal component analysis, the classical Gaussian mixture model, and the proposed algorithm show that the proposed approach has much superiority on the fault detection of turbine engine disk.

Keywords

Fault detection Gaussian mixture model expectation–maximization incremental forgetting mechanism turbine engine disk

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Fault detection for turbine engine disk using adaptive Gaussian mixture model

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