Conditional Domain Adaptation Based Anomaly Detection Method

Abstract: A method for anomaly detection in an operational asset includes collecting a source domain dataset corresponding to a first operating condition of the operational asset, wherein samples from the source domain dataset belong to a healthy class, and a faulty class; collecting a target domain dataset corresponding to a second operation condition of the operational asset, wherein samples from the target domain dataset belong to the healthy class; inputting the source domain dataset and the target domain dataset as input data into a neural network; extracting, by the neural network, features from the input data, wherein a first subset of features is discriminative of the healthy class and a second subset of features is domain invariant; reducing a dimensionality of the features into reduced features; and classifying the reduced features into a normal class and an anomaly class using a one-class classifier.

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