Linear Methods for Predictive Maintenance: The Case of NASA C-MAPSS Datasets

Predictive maintenance systems increasingly leverage diverse sensor modalities to improve failure prognostics and remaining useful life (RUL) estimation. However, integrating heterogeneous data types—vibration, temperature, acoustic, and visual sensors—typically requires complex fusion architectures. This paper proposes a unified linear classification–regression framework that addresses predictive maintenance through a shared measurement space approach. The developed method employs Linear Discriminant Analysis to establish hyperplane boundaries partitioning the measurement space into nominal, warning, and failure regions. By tracking each data point’s signed distance to these learned boundaries, this research generates continuous RUL predictions through linear regression mapping. The framework’s key innovation lies in seamlessly integrating heterogeneous sensor modalities without requiring separate preprocessing pipelines or complex fusion layers—each modality contributes to fault detection and RUL estimation based on its discriminative power in the joint feature space. While linear assumptions may simplify complex non-linear failure patterns, the proposed approach offers significant advantages in interpretability, computational efficiency, and deployment ease. Validation on the C-MAPSS turbofan engine degradation dataset demonstrates that while not achieving state-of-the-art performance, the framework provides a practical foundation accommodating data-driven, physics-based, and knowledge-based modeling paradigms within a unified architecture, making it valuable for industrial applications requiring transparent multi-modal integration.