Research on a coal cutter bearing fault diagnosis method based on LCD-SVD
WANG Yong
SUN Xiaoyu
LI Heng
Abstract：Addressing the issue that coal mining machine bearings are susceptible to noise interference under complex working conditions,leading to the failure of traditional fault diagnosis methods,this paper proposes a cooperative diagnosis method based on Local Characteristic-scale Decomposition(LCD)and Singu-lar Value Decomposition(SVD).The method first employs LCD to perform multi-scale decomposition on vibration signals,extracting Intrinsic Scale Components(ISC)containing fault information,and then filters effective ISC based on an improved envelope entropy criterion to remove redundant infor-mation.Subsequently,SVD is utilized to perform secondary noise reduction on the filtered ISC,en-hancing fault features.Finally,characteristic frequencies are extracted through envelope spectrum a-nalysis to realize the identification of different fault types.In a field test using the MG-1200 coal min-ing machine,the method successfully identified three operating conditions:normal operation,early-stage fault,and severe fault,and the extracted fault characteristic frequencies were consistent with theoretical calculations and actual conditions.The research results indicate that this method can effec-tively suppress noise interference and accurately extract weak fault features,providing a new technical means and approach for fault diagnosis of coal mining machine bearings.
Keywords：coal cutter bearingfault diagnosislocal characteristic-scale decompositionsingular value decomposition
Publication Date：2025-10-01
Online Publishing Date：2025-09-22(First online date of this platform, not the publication date of the document)
Pages：5( 52-55,89 )