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Fault detection in rotor bearing systems using time frequency techniques

机译:使用时频技术的转子轴承系统故障检测

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摘要

Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.
机译:诸如偏心,转子裂纹以及转子与定子之间的摩擦等故障可能共同存在于转子轴承系统中。转子动态人员监视和检测旋转机械中的故障是一项重要任务。在本文中,利用转子启动振动利用时频技术解决了故障识别问题。如上所述,通过对转子轴承系统进行有限元分析,并进行单个和整体故障组合来进行数值模拟。比较了三种信号处理工具,即短时傅立叶变换(STFT),连续小波变换(CWT)和希尔伯特·黄变换(HHT),以评估其检测性能。提出了信噪比(SNR)的增加对三种时频技术的影响。对比研究的重点是检测引起故障的最小可能水平以及所消耗的计算时间。与基于CWT的诊断相比,HHT消耗的计算时间非常少。但是,对于嘈杂的数据,CWT比HHT更可取。为了使用小波识别故障特征,详细介绍了调整母小波分辨率的过程。进行实验以获得转子轴承装置的启动数据,以诊断轴未对准和转子定子摩擦故障。

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