Abstract
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.
Highlights
Nuclear power, hydroelectric power, wind power, solar energy, and other energy-acquiring power generation technologies have developed rapidly, the world’s electricity supply is still dominated by thermal power and improving steam parameters of thermal power units is an effective solution to improve power generation efficiency and alleviate the energy crisis and environmental damage existing at present [1,2]
This study aims to obtain increasingly reliable remainingremaining-life prediction results, learn existing operation rules and laws using deep learnlife prediction results, learn existing operation rules and laws using deep learning methods, ing methods, and simulate the future operation trend state of the power plant boiler diand simulate the future operation trend state of the power plant boiler directly using rectly using existing power plant boiler operation data
Combined with the Weibull distribution model, the current safety conMultisource heterogeneous data generated from the actual service of power plants dition of pressurized components of the power station boiler can be evaluated and the combined with deep learning techniques were used in this study to investigate the direction future safety condition can be predicted to obtain the trend of the accumulated failure of creep, fatigue, and coupled creep–fatigue damage characteristics of key pressure-bearing risk
Summary
Hydroelectric power, wind power, solar energy, and other energy-acquiring power generation technologies have developed rapidly, the world’s electricity supply is still dominated by thermal power and improving steam parameters of thermal power units is an effective solution to improve power generation efficiency and alleviate the energy crisis and environmental damage existing at present [1,2]. Process control engineering continuously stores and accumulates these process operation data in time and extends data transmitted between and within collected devices and people in space to obtain large-capacity data at different scales in both time and space dimensions. Such massive amounts of data are poorly applied to the process of equipment health diagnosis and damage assessment. A model was established using the process’s normal operation data Based on such a model, the fault detection indicators and their control limits for fault detection could be defined for fault detection and diagnosis. Providing an accurate quantitative assessment of the damage state of in-service equipment is impossible
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