Prediction of Thermal Efficiency Degradation Trend of Steam Turbine Based on Learning Algorithms Such as Random Forest and Neural Networks

SHI Yongfeng; SHEN Weiyu; XIANG Wenguo; CHEN Shiyi

doi:10.13334/j.0258-8013.pcsee.242811

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Prediction of Thermal Efficiency Degradation Trend of Steam Turbine Based on Learning Algorithms Such as Random Forest and Neural Networks

更新时间：2026-10-12

- Prediction of Thermal Efficiency Degradation Trend of Steam Turbine Based on Learning Algorithms Such as Random Forest and Neural Networks
- Vol. 46, Issue 10, Pages: 4174-4182(2026)
- 作者机构：
  
  东南大学能源与环境学院,江苏省,南京市,211102
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.242811
  CLC：
- Published：2026
- 稿件说明：
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SHI Yongfeng, SHEN Weiyu, XIANG Wenguo, et al. Prediction of Thermal Efficiency Degradation Trend of Steam Turbine Based on Learning Algorithms Such as Random Forest and Neural Networks[J]. 2026, 46(10): 4174-4182.
DOI：

SHI Yongfeng, SHEN Weiyu, XIANG Wenguo, et al. Prediction of Thermal Efficiency Degradation Trend of Steam Turbine Based on Learning Algorithms Such as Random Forest and Neural Networks[J]. 2026, 46(10): 4174-4182. DOI： 10.13334/j.0258-8013.pcsee.242811.

摘要

为解决燃气-蒸汽联合循环机组热效率的退化精准预测问题，该文提出一种基于随机森林与向量回归及自回归神经网络的综合机器学习方法。该方法采用随机森林(random forest，RF)对采集数据进行标准工况的折算处理，通过支持向量回归(support vector regression，SVR)模拟汽轮机在健康状态下的运行参数并建立退化指标，并构建自回归神经网络(auto-regression neural net，AR-Net)汽轮机热效率退化预测模型并用来预测热效率退化趋势。结果表明，RF模型有效减轻了环境波动的影响；SVR模型准确预测了汽轮机在健康状态下的运行参数；AR-Net模型则准确预测了长时间跨度上的退化趋势。可知，该模型通过整合历史与实时数据，能够有效捕捉汽轮机热效率退化趋势，可为汽轮机的预测性维护提供一定指导。

Abstract

The long-term operation of steam turbines inevitably leads to thermal efficiency degradation. Accurately predicting the trend of steam turbine thermal efficiency degradation is crucial for optimizing maintenance cycles and enhancing operational economy and safety. Given the complexity of steam turbine operation

especially the influence of variable working conditions and environmental factors

this study proposes a comprehensive machine learning method for predicting the degradation of steam turbine thermal efficiency. This method integrates random forest (RF)

support vector regression (SVR)

and autoregressive neural networks (AR-Net). Specifically

RF is used to standardize the collected data

SVR is employed to simulate the operational parameters under healthy conditions and establish a degradation index

and finally

AR-Net is applied to construct a predictive model for the trend of efficiency degradation. The results indicate that the RF model effectively mitigates the impact of environmental fluctuations

the SVR model accurately predicts the operational parameters under healthy conditions

and the AR-Net model precisely forecasts the long-term degradation trend. In summary

by integrating historical and real-time data

the model effectively captures the trend of thermal efficiency degradation

providing guidance for predictive maintenance of steam turbines.

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