Abstract
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.
Highlights
Fault detection is an active research topic which learns a model from normal data and identifies anomalous behavior in data
We propose a novel algorithm for learning a one-class large vector-angular region and margin for fault detection of turbine engine disk. e major improvement of presented work lies in the following two aspects
We proposed two-class LARM [19] approach and applied successfully the anomalous detection problem based on imbalanced data. e two-class LARM algorithm is described as follows
Summary
Fault detection is an active research topic which learns a model from normal data and identifies anomalous behavior in data. In the classification-based anomaly detection methods, the detection model is learned from the labeled training data which can predict if the test samples are normal or abnormal. In order to effectively solve this problem, one-class classification methods have been proposed which can effectively detect the fault signal under the condition of the normal sample. E one-class SVDD algorithm is introduced by Tax and Duin [9], which is a single classification method based on minimum bounding sphere theory, can handle the fault signal classification problem only under the condition of the normal samples. In order to improve generalization ability, Wu and Ye [16] propose a small sphere and large margin (SSLM) algorithm for anomaly detection with few outlier training data.
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