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作 者:唐贵基[1] 孙军科 王晓龙[1] 伍小林 周福成[1] 崔彦亭 吴韬 胥佳瑞 TANG Guiji;SUN Junke;WANG Xiaolong;WU Xiaolin;ZHOU Fucheng;CUI Yanting;WU Tao;XU Jiarui(Hebei Key Laboratory of Electric Machinery Health Maintenance&Failure Prevention,Department of Mechanical Engineering,North China Electric Power University,Baoding 071003,China;Institute of Thermal Power Technology,China Datang Corporation Science and Technology Research Institute,Beijing 100040,China)
机构地区:[1]华北电力大学机械工程系河北省电力机械装备健康维护与失效预防重点实验室,河北保定071003 [2]中国大唐集团科学技术研究院有限公司火力发电技术研究院,北京100040
出 处:《电力自动化设备》2022年第3期121-127,167,共8页Electric Power Automation Equipment
基 金:国家自然科学基金资助项目(52005180);中央高校基本科研业务费专项资金资助项目(2021MS069);河北省自然科学基金资助项目(E2020502031,E2019502047)。
摘 要:为了自适应确定奇异谱分解(SSD)信号处理过程的奇异谱分量个数,实现信号自动分解处理,融合互信息判据对SSD的迭代停止条件进行改进。然后结合Teager能量算子解调优良的时频分辨能力以及信号特征跟踪特点,提出了基于改进SSD-Teager时频分析的引风机转子故障诊断方法。通过仿真信号分析验证所提方法对于含噪多分量信号的处理能力,并利用现场实测引风机转子不对中故障振动信号分析验证该方法的工程实用性,结果表明该方法能够精确呈现信号的整体时频特征,与传统的希尔伯特-黄变换方法相比分析效果更佳。In order to adaptively determine the singular spectrum component number during signal processing of SSD(Singular Spectrum Decomposition),and realize the automatic signal decomposition processing,the iteration stop condition of SSD is improved by fusing the mutual information criterion.Then,combined with the excellent time-frequency resolution capability and the signal feature tracking trait of Teager energy operator demodulation,the induced draft fan rotor fault diagnosis method based on improved SSD-Teager time frequency analysis is proposed.The ability for processing multi-component signal with noise of the proposed method is verified by the simulation signal analysis,and the practicability of the proposed method is also verified by the field measured vibration signal of the rotor induced draft fan misalignment fault.The results show that the proposed method can accurately present the whole time-frequency characteristics of the signal,and the analysis effect is much better than the traditional Hilbert-Huang transform method.
关 键 词:引风机转子 故障诊断 改进奇异谱分解 Teager时频分析
分 类 号:TM31[电气工程—电机] TH133[机械工程—机械制造及自动化]
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