A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Fault Detection and Control Systems · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Big Data, Baoshan University, Baoshan 678000, China
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Funding
Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities’ Association · Collaborative education project of industry university cooperation of the Ministry of Education · 10th batches of Baoshan young and middle-aged leaders training project in academic and technical · PhD research startup foundation of Yunnan Normal University
References (22)
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