Open AccessAn SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method
Author(s) -
Zhu Xiao-hong,
Jianhong Zhao,
Dibo Hou,
Zhonghe Han
Publication year - 2019
Publication title -
shock and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 45
eISSN - 1875-9203
pISSN - 1070-9622
DOI - 10.1155/2019/3926963
Subject(s) - vibration , convolutional neural network , fault (geology) , modal , rotor (electric) , identification (biology) , computer science , information fusion , modal analysis , artificial intelligence , pattern recognition (psychology) , fusion , control theory (sociology) , engineering , acoustics , physics , biology , geology , mechanical engineering , chemistry , botany , control (management) , seismology , polymer chemistry , linguistics , philosophy
This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.