Research on intelligent monitoring and diagnosis system of equipment condition based on big data and deep neural network

This paper studies an intelligent monitoring and diagnosis system of tobacco equipment based on big data and deep neural network (DNN). Aiming at the shortcomings of traditional monitoring methods in data utilization, diagnosis accuracy and response speed, an intelligent monitoring and diagnosis scheme is proposed, which combines multi-source sensor data and automatically extracts features by DNN. The system architecture includes multi-source data acquisition and preprocessing module, cross-modal feature fusion module, CNN-LSTM mixed model modeling module and adaptive early warning module. By integrating multi-source sensor data such as vibration, temperature, energy consumption and acoustics, a panoramic view of equipment operation state is constructed to realize cross-modal information fusion, CNN-LSTM hybrid model is used to learn and extract the spatial and temporal features directly from the original data, forming an end-to-end diagnosis framework. The monitoring threshold is dynamically adjusted based on the prediction results of the model, and the adaptive early warning strategy is realized. In the experiment, the data of three years' operation of tobacco cutter and winder in a tobacco factory were used, covering vibration, temperature, acoustics and current signals, and 12 typical faults were recorded. The results show that the system achieves 95.3% accuracy and 91.7% F1 score on the test set, and keeps the false alarm rate at 2.9%, which is significantly better than traditional methods and other deep learning methods. The ablation experiment further verified the importance of multi-source data fusion and dynamic threshold adjustment in improving the accuracy of equipment condition monitoring and fault diagnosis. This research provides new ideas and methods for intelligent monitoring and diagnosis of tobacco equipment, and has broad application prospects.