Open AccessA Novel Median-Point Mode Decomposition Algorithm for Motor Rolling Bearing Fault Recognition
Author(s) -
Ganzhou Yao,
Bo Fan,
Wen Wang,
Haihang Ma
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/9406479
Subject(s) - bearing (navigation) , sort , fault (geology) , mode (computer interface) , envelope (radar) , control theory (sociology) , algorithm , inversion (geology) , point (geometry) , decomposition , engineering , computer science , pattern recognition (psychology) , mathematics , artificial intelligence , telecommunications , paleontology , radar , ecology , geometry , control (management) , structural basin , seismology , biology , information retrieval , geology , operating system
Precise fault recognition of motor rolling bearing fault is playing a significant role in any machinery and equipment. However, conventional decomposition methods fail to completely reveal the fault signal information of motor rolling bearing due to mixed modes problem. To solve the problem, the median-point mode decomposition (MMD) method is presented. The MMD method uses sort-based inversion to sort out each variation of the same time interval for better and specific mode decomposition, with the assistance of the advanced envelope curve formed by the median points between adjacent extreme points. It certainly alleviates the mixed mode during the iteration of intrinsic mode functions (IMFs). Therefore, comparison results are simulated in the proposed MMD method with conventional methods. Experiment of motor rolling bearing fault is operated for fault recognition in order to demonstrate the MMD algorithm.