Synchrosqueezing transform based general linear chirplet transform of instantaneous rotational frequency estimation for rotating machines with speed variations

Author(s):  
Yi Liu ◽  
Zhansi Jiang ◽  
Gang Wang ◽  
Jiawei Xiang
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Liang Guo ◽  
Yingqi Huang ◽  
Hongli Gao ◽  
Li Zhang

Ball screw, as a crucial component, is widely used in various rotating machines. Its health condition significantly influences the efficiency and position precision of rotating machines. Therefore, it is important to accurately detect faults and estimate fault location in a ball screw system to make sure that the ball screw system runs safely and effectively. However, there are few research studies concerning the topic. The aim of this paper is to fill the gap. In this paper, we propose a method to automatically detect and locate faults in a ball screw system. The proposed method mainly consists of two steps: fault time estimation and instantaneous rotational frequency extraction. In the first step, a statistics-based outlier detection method is proposed to involve the fault information mixing in vibration signals and estimate the fault time. In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. Once the fault time and instantaneous rotational frequency are estimated, the fault location in a ball screw system is calculated through an integral operation. In order to verify the effectiveness of the proposed method, two fault location experiments under the constant and varying speed conditions are conducted in a ball screw failure simulation testbed. The results demonstrate that the proposed method is able to accurately detect the faults in a ball screw system and estimate the fault location within an error of 22%.


2016 ◽  
Vol 70-71 ◽  
pp. 958-973 ◽  
Author(s):  
Gang Yu ◽  
Yiqi Zhou

2020 ◽  
Vol 19 (6) ◽  
pp. 2051-2062
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Saqlain Abbas ◽  
Fucai Li

Time–frequency analysis is recognized as an efficient tool to characterize the time-varying feature from the oscillatory signal by transforming it into an identifiable form. Some traditional time–frequency transforms are subjected to poor time–frequency resolution or do not allow for mode reconstruction. As a postprocessing method, the synchrosqueezing transform has been utilized to tackle these problems. In this framework, a new method termed as generalized wavelet-based synchrosqueezing transform is developed in the current research work to deal with a strong modulated signal. The proposed method is capable to theoretically generate unbiased instantaneous frequency estimation at any order by defining a higher-order Taylor expansion signal model. The signal mapping procedure is also embedded in the algorithm to further improve the anti-noise robustness of the presented method. Numerical investigation of synthetic signal verifies the feasibility of the generalized wavelet-based synchrosqueezing transform as compared to previously developed approaches. Moreover, the practical implementation of the proposed method for the detection of the rotor rub-impact fault demonstrates that the generalized wavelet-based synchrosqueezing transform is qualified for machine fault diagnosis under the variable speed conditions.


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