Research on Magnetic Flux Leakage Detection Data Analysis and Corrosion Defect Identification Model for Boiler Water Walls Based on Deep Learning

Boiler water wall tubes, as key components of thermal power plants' core equipment, are subjected to high-temperature and high-pressure steam and flame erosion over long periods, making them prone to corrosion and wear defects that can threaten operational safety. This study aims to enhance the accuracy and efficiency of magnetic flux leakage detection data analysis for boiler water walls through deep learning technology. The research employs high-precision magnetic flux leakage detection instruments and sensor arrays to collect data, and use the aerial work platform to carry out aerial work with a detector to improve the efficiency of data acquisition. Data processing includes noise reduction, normalization, and feature extraction, involving both time-domain and frequency-domain analyses. The deep learning-based model consists of a deep convolutional neural network (DCNN) and a regression module, achieving automatic feature extraction and quantitative defect identification. The model was trained on 10,000 samples, achieving an accuracy rate of over 95%. Experimental results show that the model performs excellently in identifying normal samples but has room for improvement in identifying defective samples. Compared with traditional methods, the deep learning-based model demonstrates significant advantages in terms of recognition accuracy and efficiency.