Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Korea.
Department of Mechanical Engineering, Seoul National University, Seoul 08826, Korea.
Sensors (Basel). 2020 Nov 7;20(21):6356. doi: 10.3390/s20216356.
Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model's effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.
锅炉水冷壁管泄漏是蒸汽动力厂（SPP）失效的最可能原因。智能管泄漏检测系统的开发可以提高现代电厂的效率和可靠性。基于多元算法的电子维护的理念最近被引入到 SPP 的智能故障检测和诊断中。然而，这些多元算法高度依赖于输入过程变量（传感器）的数量。因此，这项工作提出了一种基于机器学习的模型，该模型与最优传感器选择方案相结合，用于分析锅炉水冷壁管泄漏。最后，采用实际的 SPP 案例来验证所提出模型的有效性。结果表明，与其他可比模型相比，所提出的模型可以成功检测到水冷壁管泄漏，并提高了准确性。
