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Application of deep learning in early warning of equipment failure in water logging disaster scenarios

Ma Nan, Liu Guowei, Wang Yijun, Xin Lisheng

Diagnostyka

2025

Vol. 26, No. 3

art. no. 2025311

Water logging disaster is a challenge for the city, and equipment failure is the main factor leading to the expansion of the disaster. In order to improve the accuracy and timeliness of fault prediction, this study proposes a device failure early warning method based on deep learning, to provide an effective risk management means for urban infrastructure. Using a hybrid model combining a constitutional neural network (CNN) and a long short-term memory network (LSTM), multi-dimensional sensor data from drainage systems, power supply systems, transportation systems, and communication systems are processed, and the results are analyzed, for prediction and early warning of equipment failures. Historical equipment failure records, real-time monitoring data and meteorological information were collected and input into the model for training and testing after cleaning and p reprocessing. The research results show that the model has excellent performance in many evaluation indicators such as equipment failure prediction accuracy, recall rate and F1 value, and can warn equipment failures in advance, provide sufficient time for emergency treatment and equipment maintenance.