Abstract
As an important part of rotating machinery, a healthy rotor is critical to ensuring optimal working conditions of the entire system. Considering that the vibration signal of rotor consists of different frequency components when the failure arises, a novel rotor failure detection method based on singular spectrum decomposition (SSD) is presented. The original vibration signal is adaptively decomposed into a number of singular spectrum components (SSCs) by the SSD method. Then, energy separation algorithm (ESA) is adopted to demodulate each singular spectrum component. Finally, the SSD-ESA time-frequency spectrum can be obtained and the fault features contained in the SSD-ESA time-frequency spectrum can be identified to determine the fault types. The effectiveness of SSD for harmonic separation was assessed through tones separation analyses, the results show that SSD is able to separate more harmonic pairs of different amplitude ratios than empirical mode decomposition (EMD). Furthermore, three simulations of multi-component signals were designed to investigate the use of SSD for signal decomposition. The SSD method was then applied to detect signatures caused by rotor oil film whirl in experimental signals and compared to both EMD and ensemble EMD (EEMD). The simulated analysis results reflect that SSD shows superiority to EMD and EEMD in inhibiting mode mixing and extracting the time-varying frequency components. The experimental analysis results demonstrate that the SSD based rotor failure detection method is an alternative method under both constant and variable speed conditions.
Highlights
As a main transmission and bearing mechanism of rotating machinery, the rotor plays a pivotal role in the industrial production [1]
In order to separate the harmonic signals successfully, varieties of signal decomposition methods have been applied in rotor failure detection, such as empirical mode decomposition (EMD) [4], ensemble EMD (EEMD) [5], local mean decomposition (LMD) [6], ensemble LMD (ELMD) [7, 8], intrinsic time-scale decomposition (ITD) [9], local characteristic-scale decomposition (LCD) [10], complete overall LCD (CELCD) [11], differential-based EMD (DEMD) [12], differential-based EEMD (DEEMD) [13], etc
The decomposed components of SSC1 and SSC3 obtained by singular spectrum decomposition (SSD), IMF1 and IMF3 obtained by EMD, IMF1 and IMF3 obtained by EEMD are selected to compute the root mean squared error (RMSE) and CC indexes with the corresponding true components of x (t)
Summary
As a main transmission and bearing mechanism of rotating machinery, the rotor plays a pivotal role in the industrial production [1]. In order to separate the harmonic signals successfully, varieties of signal decomposition methods have been applied in rotor failure detection, such as empirical mode decomposition (EMD) [4], ensemble EMD (EEMD) [5], local mean decomposition (LMD) [6], ensemble LMD (ELMD) [7, 8], intrinsic time-scale decomposition (ITD) [9], local characteristic-scale decomposition (LCD) [10], complete overall LCD (CELCD) [11], differential-based EMD (DEMD) [12], differential-based EEMD (DEEMD) [13], etc While these signal decomposition methods have solved some rotor fault diagnosis problems effectively in the applications, envelope fitting must be performed during. Combining the signal decomposition methods with the demodulation techniques to perform the time-frequency analysis has been frequently used for rotor failure detection.
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