Interpretable Generative Model for Aero-Engine Fault Clustering

Abstract

Data-driven models are increasingly used to monitor complex industrial systems under varying operating conditions; however, their practical adoption is often constrained by limited labeled data and a lack of interpretability from an end-user perspective, where low-rank representations and context-irrelevant diagnosis are preferred. This study proposes a generative modeling framework for aero-engine fault clustering that emphasizes user-oriented interpretability, bridging data-driven learning with engineering diagnostic practice. The model adds a low-rank discriminative regularization to a mixture-based conditional variational autoencoder, enabling context-free clustering of multiple fault types. First, the learned latent structure yields compact and separable representations for multiple faults. Second, the associated covariance patterns are mapped back to sensor correlations within a reference context, aligning with how maintenance engineers recognize fault symptoms. Experimental results on both N-CMAPSS and real-world aero-engine datasets verify that the proposed approach yields superior clustering performance and reveals interpretable degradation patterns, supporting reliable diagnosis and informed decision-making in complex system health management.