Research on multi-source signal fusion diagnosis technology for abnormal vibration in main steam turbines
主汽机振动异常的多源信号融合诊断技术研究

摘要

主汽机振动异常诊断是保障电力系统稳定运行的关键技术之一。为了有效识别主汽机在运行过程中的振动异常并提高诊断准确性，本文提出了一种基于多源信号融合的诊断技术。通过结合振动信号、温度信号以及压力信号，采用信号处理与融合算法，能够提高异常诊断的鲁棒性和精度。本文研究了多种信号融合方法的优缺点，分析了各类信号在诊断过程中的互补性，提出了一种新的多源信号融合方法。实验结果表明，该方法能够准确识别主汽机的振动异常，为电力设备的维护和故障预防提供了有效支持。

Abstract

Diagnosing abnormal vibration in main steam turbines is one of the key technologies for ensuring the stable operation of power systems. To effectively identify vibration abnormalities during operation and improve diagnostic accuracy, this paper proposes a diagnosis technique based on multi-source signal fusion. By integrating vibration signals, temperature signals, and pressure signals, and employing signal processing and fusion algorithms, the robustness and accuracy of anomaly diagnosis can be enhanced. This paper investigates the advantages and disadvantages of various signal fusion methods, analyzes the complementarity of different signals in the diagnostic process, and proposes a novel multi-source signal fusion approach. Experimental results demonstrate that the proposed method can accurately identify abnormal vibrations in main steam turbines, providing effective support for the maintenance and fault prevention of power equipment.