Multivariable correlation feature network construction and health condition assessment for unlabeled single-sample data

The construction of effective health indicators is crucial for assessing system degradation, enabling anomaly detection and health condition assessment, which contribute to reducing costs, improving productivity, and enhancing system availability. However, there are notable challenges in the health condition assessment for unlabeled single-sample data. In this paper, a multivariable correlation feature network—the Nested Autoencoder (NAE) network, is proposed, which incorporates inter-variable correlations to constrain the construction of latent space, thereby enhancing the accuracy of health condition assessment. Moreover, the autoencoder parameters calculated with each discrete interval data are leveraged to construct latent feature graphs, enabling component anomaly detection. Furthermore, a comprehensive indicator is introduced to describe the topological changes of the graphs, facilitating the assessment of the health conditions of the system. Finally, the effectiveness of our method is validated on the N-CMAPSS dataset as well as a real satellite dataset.