Condition Monitoring with Time Series Data Based on Probabilistic Model
Jaehyun Soh, DaeEun Kim · 2021 24th International Conference on Electrical Machines and Systems (ICEMS) · 2021
As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.