Abstract
Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.
Highlights
Fault diagnosis plays a vital role in ensuring long-term safe running of rotating machinery for avoiding huge economic loss and casualties. us, many fault diagnosis methods based on the collected monitoring signals such as sound [1], infrared images [2], and current [3] are carried out
Of all the different types of monitoring signal, vibration signal contains abundant information of machinery health conditions and is the most analyzed signal, and many signal processing based methods have been proposed to process vibration signals for fault diagnosis [4, 5]. e abundant information refers to the repetitive transients generated periodically when faults occur on the rotating machinery [6]
Vibration signals containing repetitive transients can be used for diagnosing the fault, machinery usually operates under harsh environment and the transients are submerged in noise
Summary
Fault diagnosis plays a vital role in ensuring long-term safe running of rotating machinery for avoiding huge economic loss and casualties. us, many fault diagnosis methods based on the collected monitoring signals such as sound [1], infrared images [2], and current [3] are carried out. To effectively extract repetitive transients from signals suffered from heavy noise including the Gaussian white noise and the impulsive noise, a new fault diagnosis method is proposed based on the optimized Morlet wavelet and the square envelope spectrum entropy. (1) An optimized SES entropy wavelet is proposed to extract the repetitive transients for fault diagnosis, and the proposed method presents good performance even when impulsive noise is mixed in the diagnosed signal (2) e Morlet wavelet function which is similar to impulsive signals is used and its parameters are optimized and selected by calculating the Shannon entropy of wavelet coefficients (3) Square envelope spectrum entropy is introduced for guiding the selection of wavelet scales e rest of this paper is organized as follows.
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