汽轮发电机组振动故障诊断技术研究进展

doi:10.12096/j.2096-4528.pgt.21048

摘要/Abstract

摘要：

高参数大容量汽轮发电机组的安全稳定运行对电力生产具有重要意义。综述了汽轮发电机组振动故障的机理、信号检测、信号分析、特征提取以及故障诊断方法。针对传统的智能诊断方法面临采样数据量大、信号特征提取困难、故障训练样本不足等问题，介绍了先进的传感技术和以深度学习为代表的新一代智能机器学习技术。通过分析得出结论：未来汽轮发电机组振动故障诊断技术应以人工智能、大数据、云计算等技术为核心，融合虚拟化及三维可视化技术，实现故障诊断的速度与精度相统一。

关键词: 汽轮发电机组, 特征提取, 故障诊断, 人工智能, 大数据, 云计算, 深度学习

Abstract:

With the increasing demand of power energy, the safe and stable operation of high parameter and large capacity turbo-generator sets is of great significance to power production. The vibration fault mechanism, signal detection, signal analysis, feature extraction and fault diagnosis methods of turbine generator set were summarized, respectively. Moreover, an advanced sensing technology and a new generation of intelligent machine learning technology represented by deep learning were introduced to solve the problems that traditional intelligent diagnosis methods are faced with, such as large amount of sampled data, difficulty in extracting signal features and shortage of fault training samples. It is summarized that the future vibration fault diagnosis technology of turbo generator sets should be based on artificial intelligence, big data, and cloud computing, supplemented by fusion virtualization and three-dimensional visualization technology, to achieve the unity of fault diagnosis speed and accuracy.

Key words: steam turbine generator set, feature extraction, fault diagnosis, artificial intelligence, big data, cloud computing, deep learning

中图分类号:

TK05

陈尚年, 李录平, 张世海, 欧阳敏南, 樊昂, 文贤馗. 汽轮发电机组振动故障诊断技术研究进展[J]. 发电技术, 2021, 42(4): 489-499.

Shangnian CHEN, Luping LI, Shihai ZHANG, Minnan OUYANG, Ang FAN, Xiankui WEN. Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets[J]. Power Generation Technology, 2021, 42(4): 489-499.

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