Multimodal Time Series Forecasting for Online Oil Monitoring of Petrochemical Pelletizer Gearbox Using Multiscale Inverted Transform Network

The large petrochemical pelletizer gearbox is a critical component in synthetic chemicals, accurate forecasting of its time series from online oil monitoring is of utmost importance for safe operation. However, existing time series forecasting methods face challenges in handling oil monitoring scenarios characterized by disturbances, multimodality, and large time spans. To address these issues, we propose an intelligent forecasting method named multiscale inverted transform network (MITN). First, the multimodal online oil monitoring time series collected from moisture sensors, viscosity sensors, abrasive image sensors, and metal particle sensors are utilized to perform correlation analysis and identify the key nonlinear variables. In addition, the multiscale module is further employed to obtain comprehensive redundant characteristics. Second, the inverted perspective is designed for modeling from the time dimension and the variable dimension. Third, the multiple multiattention mechanism module is utilized with the feedforward network to learn the semantic meaning of variable time series and produce accurate forecasting results. Finally, layer normalization is used to improve the training stability and convergence to eliminate the distribution difference between variables. We used online oil monitoring time series from a petrochemical pelletizer gearbox from July 2018 to May 2023 for validation. The results show that MITN not only can obtain smaller forecasting errors than the existing time series forecasting networks, such as long short-term memory, gated recurrent unit, temporal convolutional network, and transform, but also can effectively generalize to the unknown variables. The proposed MITN pioneers ideal multivariate time series forecasting for complex online oil monitoring, with the potential to enhance the operational safety of large petrochemical plants.