电厂汽轮机高中压转子振动突变故障识别研究被引量：1

Study on Sudden Vibration Fault Identification of High and Intermediate Pressure Rotor of Steam Turbine in Power Plant

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摘要为了保证电厂汽轮机能够在高温、高转速环境中安全稳定运行,提出电厂汽轮机高中压转子振动突变故障识别方法.根据电厂汽轮机高中压转子振动突变故障时的轴系振动特点,利用一体化电涡流位移传感器采集相应的故障信号,利用小波包分析提取高中压转子振动突变故障特征,将提取到的故障特征作为输入量,输入采用人工鱼群算法优化的RBF神经网络中,输出电厂汽轮机高中压转子振动突变故障类型识别结果.在实验过程中对质量不平衡、转子热弯曲、转轴不对中、转动部件飞脱、动静碰磨、汽流激振、结构共振、结构刚度不足、转子裂纹等9种常见故障进行识别.实验结果表明,该方法分解并重构的电厂汽轮机高中压转子振动突变故障信号质量较高,获得故障识别结果与实际故障相同,识别精度高,结果具备可靠性. In order to ensure the safe and stable operation of the power plant steam turbine in the high temperature and high speed environment,a sudden vibration fault identification method of high and intermediate pressure rotor of steam turbine in power plant was proposed.According to the shafting vibration characteristics of the high and medium pressure rotor vibration of the power plant turbine,the integrated eddy current displacement sensor was used to collect the corresponding fault signal,and the wavelet packet analysis was used to extract the high and medium pressure rotor vibration mutation fault characteristics,and the extracted fault characteristics were used as input.Input the fault features into the RBF neural network optimized by artificial fish swarm algorithm,and output the identification result of the vibration mutation type of the high and medium pressure rotor of the steam turbine of the power plant.During the experiment,the method in this paper was used to identify 9 common faults,such as mass unbalance,rotor thermal bending,shaft misalignment,rotating parts flying off,dynamic and static friction,steam flow excitation,structural resonance,insufficient structural rigidity,and rotor cracks.The experimental results showed that the method decomposes and reconstructs the high-pressure rotor vibration mutation fault signal of the power plant steam turbine with high quality,and the obtained fault identification result was the same as the actual fault,the identification accuracy was high,and the result was reliable.

作者曾娜 ZENG Na(School of Power Engineering,Anhui Electrical Engineering Vocational and Technical College,Anhui 230051,China)

机构地区安徽电气工程职业技术学院动力工程系

出处《吉林化工学院学报》 CAS 2022年第7期94-99,共6页 Journal of Jilin Institute of Chemical Technology

基金安徽省质量工程项目“课程思政融于专业核心课程(汽轮机运行)的实践与探索”(2021jyxm0104)。

关键词电厂汽轮机高中压转子振动突变故障识别小波包分析 RBF神经网络 power plant steam turbine high and intermediate pressure rotor vibration mutation fault identification wavelet packet analysis RBF neural network

分类号 TP212 [自动化与计算机技术—检测技术与自动化装置]