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State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a statedoi:10.3390/s20236839 pmid:33265918 fatcat:gbmdx3kuuvadnmsubgku3q4o64