An enhanced hybrid deep neural network method for adjusted industrial time series prediction with variable operating states

In industrial production, dynamic nature of working conditions and reliance on manual judgment introduces significant hurdles for accurate prediction models. Despite commendable performance of contemporary Deep Learning techniques in time series prediction (TSP), they frequently overlook crucial impact of human intervention. Moreover, the subjective nature of operational condition labeling and the scarcity of comprehensive experimental datasets further hinder the efficacy of predictive systems. This work proposes an Enhanced Hybrid Deep Neural Network (EH-DNN) framework to tackle these issues. It achieves robust classification and prediction of working conditions by integrating the multi-dimensional features of set values and observation time series. The data preprocessing phase encompasses feature extraction and feature fusion, ensuring the model acquires the essential information intrinsic to the production process. A novel two-step prediction methodology is employed during the training phase, incorporating pre-classification to enhance TSP, achieving an accuracy of 94%. EH-DNN mirrors intricate dynamics of industrial production and aligns seamlessly with real-world application scenarios, demonstrating substantial practical utility. By integrating this methodology, the industrial sector can anticipate a significant leap in automation levels and production efficiency, bridging the gap between theoretical models and practical implementation.