Multi-Source Risk Data Fusion Monitoring Algorithm for Hydrogen Equipment based on Deep Learning

The monitoring data sources for monitoring the health status of hydrogen energy equipment are diverse and have strong temporal and spatial correlations. Traditional methods are difficult to effectively fuse and analyze multi-source data. To this end, this paper applies deep learning methods to construct a multi-source data fusion monitoring algorithm based on hybrid convolutional neural network-long short-term memory network (Hybrid CNN-LSTM). CNN is responsible for extracting spatial features such as crack morphology in ultrasonic detection images, hydrogen bubbling areas, and temperature anomaly distribution in infrared thermal imaging data; LSTM is responsible for analyzing time series data such as pressure, temperature, and vibration to capture the long-term evolution trend of equipment status. The experimental results show that the MSE of the laboratory test data set is the lowest (training set 0.0152, test set 0.0209, and validation set 0.0193), indicating that the model can stably learn the crack propagation law under a controlled environment.