Abstract
Aiming at the problem of fault feature extraction of a diaphragm pump check valve, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet transform is proposed. Firstly, the collected vibration signal was decomposed by LMD. After several amplitude modulation (AM) and frequency modulation (FM) components were obtained, the effective components were selected according to the Kullback-Leible (K-L) divergence of all component signals for reconstruction. Then, wavelet packet transform was used to denoise the reconstructed signal. Finally, the characteristics of the fault signal were extracted by Hilbert envelope spectrum analysis. Through experimental analysis, the results show that compared with other traditional methods, the proposed method can effectively overcome the phenomenon of mode aliasing and extract the fault characteristics of a check valve more effectively. Experiments show that this method is feasible in the fault diagnosis of check valve.
Highlights
With the vigorous development of mineral pipeline transportation technology, highpressure diaphragm pump operation, maintenance, and fault monitoring have become a concern
Based on the above analysis, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet denoising is proposed in this paper
1.563 and 1.875 Hz) in the envelope spectrum of the signal after noise reduction based on quency in the Hilbert envelope spectrum obtained by the proposed method was rela wavelet packet, which has become the dominant frequency of the vibration signal, through the theLMD
Summary
With the vigorous development of mineral pipeline transportation technology, highpressure diaphragm pump operation, maintenance, and fault monitoring have become a concern. The local mean decomposition (LMD) [6] proposed by Smith is a new time-frequency analysis method, which can adaptively decompose the signal to be processed into several product functions (PF) with high to low frequency and has been gradually applied. Sun W et al [17] used a wavelet packet to remove the noise in the collected bearing signal and combined it with the LMD method to extract the fault feature. Based on the above analysis, a fault feature extraction method based on local mean decomposition (LMD) and wavelet packet denoising is proposed in this paper.
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