KEYWORDS: Data transmission, Computer security, Data communications, Control systems, Symmetric-key encryption, Data processing, Data conversion, Intelligence systems
The torpedo car (TPC) plays a vital role in the safe transportation of molten iron in steel manufacturing. Due to its longterm operation in high-temperature environments and frequent use, it is prone to a variety of faults. However, traditional manual judgment methods are not effective in fault diagnosis and have low safety factors. Through engineering practice and data visualization analysis, it is found that the temperature data of the torpedo car have a strong time dependence and are closely related to the occurrence of its faults. Therefore, this paper selects the temperature data from the working status data of a torpedo car in a large steel plant. On the basis of effective data preprocessing and sample construction, the multilayer ATT-GRU network model based on the attention mechanism using timing features is proposed for its fault diagnosis. The model uses a multilayer GRU network to extract the temperature sequence characteristics of different time segments, and the attention mechanism is used to focus on the part of the input that contributes a greater amount to the fault diagnosis results, so as to improve the diagnostic effect of the model on the type of the torpedo car faults. Compared with traditional methods, experiments show that this method significantly improves the convergence speed of model training and diagnosis accuracy. It provides a friendly solution for real-time and accurate fault diagnosis of the torpedo car and other industrial equipment.
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