Industrial Compressor-Monitoring Data Prediction Based on LSTM and Self-Attention Model

The compressor is a critical component of cryogenic separation industrial systems, and its reliable operation is essential for the stable functioning of the entire system. Existing research focuses on predicting compressor conditions using experimental or simulation data, often neglecting analysis based on actual industrial data. To address this gap, this paper presents an innovative prediction model for compressor data utilizing an LSTM–self-attention mechanism, specifically designed for real industrial systems. The proposed model combines the sequential learning capabilities of LSTM with the dynamic feature extraction strengths of self-attention, enabling highly accurate predictions by enhancing sensitivity to critical data features. The actual vibration data from a compressor spanning 12 months are analyzed and compared. The results demonstrate that the proposed model reduces prediction error by $46\%$ in MSE and $50\%$ in RMSE compared to SVR. These findings highlight the model’s potential for real-time monitoring and early fault detection, providing a robust theoretical foundation for condition-based analysis and preventive maintenance in industrial compressor applications.