Hydraulic System Failure Prediction Method with Limited Failure Data

Hydraulic systems in production equipment are relatively complex, involving various time series sensor data, such as operating sound frequencies and hydraulic pressure. However, fault prediction generally faces the bottleneck of insufficient fault data, leading to a severe imbalance between normal and fault data ratios. This imbalance makes it difficult for prediction models to effectively learn fault trends. In this paper, we propose a synthesis method for time series data to address this issue. The experimental results demonstrate that the synthesized data lead to significant improvements.