M<small>ENTOR</small>PDM: Learning Data-Driven Curriculum for Multi-Modal Predictive Maintenance

Abstract

Predictive Maintenance (PDM) systems are essential for preemptive monitoring of sensor signals to detect potential machine component failures in industrial assets such as bearings in rotating machinery. Existing PDM systems face two primary challenges: 1) Irregular Signal Acquisition, where data collection from the sensors is intermittent, and 2) Signal Heterogeneity, where the full spectrum of sensor modalities is not effectively integrated. To address these challenges, we propose a Curriculum Learning Framework for Multi-Modal Predictive Maintenance – MentorPDM. MentorPDM consists of 1) a graph-augmented pretraining module that captures intrinsic and structured temporal correlations across time segments via a temporal contrastive learning objective and 2) a bi-level curriculum learning module that captures task complexities for weighing the importance of signal modalities and samples via modality and sample curricula. Empirical results from MentorPDM show promising performance with better generalizability in PDM tasks compared to existing benchmarks. The efficacy of the MentorPDM model will be further demonstrated in real industry testbeds and platforms.