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Fault detection is an important technique to detect divergence based on unknown abnormalities, which involves establishing a computational model exclusively originated from the key features of the normal samples. The multimodality of process data distribution of engine turbine disk is inevitably affected by incorporation of ambient disturbance; the mean and covariance would vary significantly, resulting in decayed detecting accuracy. By adopting a strategy to maximize vector-angular mean and minimize vector-angular variance simultaneously in the feature space, a one-class large vector-angular region and margin (one-class LARM) framework is systematically conducted for fault detection of turbine engine disk which will enhance the robustness of the dynamic multimode process monitoring. Simulation based on the single mode and multimode of turbine engine disk is thoroughly performed and compared that the results of which solidly validated the favorable efficiency of the proposed method.

Fault Detection for Turbine Engine Disk Based on One-Class Large Vector-Angular Region and Margin