|Table of Contents|

Early fault diagnosis of vibrating screen bearings based on improved FNLM and HSEDO(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2025年01期
Page:
167-178
Research Field:
汽车与机械工程
Publishing date:

Info

Title:
Early fault diagnosis of vibrating screen bearings based on improved FNLM and HSEDO
Author(s):
WANG YAN1;  YE Min2;  LI Jia-bo1;  ZHANG Cui-hong1;  BU Peng-hui1
(1. Xi'an Key Laboratory of Wellbore Integrity Evaluation, Xi'an Shiyou University, Xi'an 710065,Shaanxi, China; 2. Key Laboratory of Expressway Construction Machinery of Shaanxi Province,Chang'an University, Xi'an 710061, Shaanxi, China)
Keywords:
mechanical engineering;  vibrating screen bearing;  fault diagnosis;  IFNLM;  HSEDO
PACS:
U415.5
DOI:
10.19721/j.cnki.1671-8879.2025.01.014
Abstract:
A fault feature extraction method of rolling bearings based on improved fast nonlocal mean filtering(IFNLM)and high-order symmetric envelope derivative operator(HSEDO)was proposed to address the diagnostic challenge that weak early fault features of vibrating screen rolling bearings often being overwhelmed by heavy background noise and multiple vibration sources. Firstly, based on the periodic structural characteristics of the rolling bearing fault signals, an IFNLM algorithm was proposed by improving the similarity measure and weight assignment function of FNLM, which can filter the background noise and vibration source interference in the original vibration signals of the bearing. Secondly, based on the envelope derivative operator(EDO), integrating the symmetric difference technique and higher order idea, the HSEDO was proposed to demodulate the denoised signal and identify the bearing fault characteristics. Finally, the feasibility and superiority of the proposed method were verified by the vibrating screen exciter rolling bearing fault test rig. The research results show that the IFNLM algirithm has a good noise removal effect, noise robustness, and operation efficiency. The running speed of the IFNLM algorithm after improvement is 77% higher than before, about 12 times the wavelet threshold denoising algorithm and 4 times the median filtering algorithm. The HSEDO algorithm has outstanding demodulation and anti-interference capabilities, and its performance is superior to that of the EDO under no more than 20 types of harmonic interference. Compared with existing methods such as the NLM + envelope spectrum and WTD + TEO, the proposed IFNLM-HSEDO algorithm has higher fault feature extraction capability and execution efficiency. When 50 000 sampling points are taken, the running time is only 0.050 8 s, and the more sampling points, the more prominent the superiority of its fast performance. The research results are of great significance to enrich the theory of mechanical system fault diagnosis and ensure the safe and reliable operation of the equipment.4 tabs, 12 figs, 28 refs.

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Last Update: 2025-02-25