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The superheater and re-heater piping components in supercritical thermal power units are prone to creep and fatigue failure fracture after extensive use due to the high pressure and temperature environment. Therefore, safety assessment for superheaters and re-heaters in such an environment is critical. However, the actual service operation data is frequently insufficient, resulting in low accuracy of the safety assessment. Based on such problems, in order to address the issues of susceptibility of superheater and re-heater piping components to creep, inaccurate fatigue failure fracture, and creep–fatigue coupling rupture in a safety assessment, their remaining life prediction and reliability, as well as the lack of actual service operation data, multisource heterogeneous data generated from actual service of power plants combined with deep learning technology was used in this paper. As such, three real-time operating conditions’ data (temperature, pressure, and stress amplitude) during equipment operation are predicted by training a deep learning architecture long short-term memory (LSTM) neural network suitable for processing time-series data and a backpropagation through time (BPTT) algorithm is used to optimize the model and compared with the actual physical model. Damage assessment and life prediction of final superheater tubes of power station boilers are carried out. The Weibull distribution model is used to obtain the trend of cumulative failure risk change and assess and predict the safety condition of the overall system of pressurized components of power station boilers.

Typical Damage Prediction and Reliability Analysis of Superheater Tubes in Power Station Boilers Based on Multisource Data Analysis