AI RESEARCH
Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction
arXiv CS.LG
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ArXi:2506.17036v2 Announce Type: replace-cross Modern industrial systems are often subject to multiple failure modes, and their conditions are monitored by multiple sensors, generating multiple time-series signals. Additionally, time-to-failure data are commonly available. Accurately predicting a system's remaining useful life (RUL) requires effectively leveraging multi-sensor time-series data alongside multi-mode failure event data. In most existing models, failure modes and RUL prediction are performed independently, ignoring the inherent relationship between these two tasks.