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Title: Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.
 [1] ;  [2] ;  [3] ;  [4]
  1. Southeast Univ., Nanjing (China). School of Instrument Science and Engineering
  2. Xi'an Jiaotong Univ. (China). School of Mechanical Engineering
  3. South China Univ. of Technology (SCUT), Guangzhou (China). School of Mechanical and Automotive Engineering
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1024-123X
Accepted Manuscript
Journal Name:
Mathematical Problems in Engineering
Additional Journal Information:
Journal Volume: 2014; Journal Issue: 2014; Journal ID: ISSN 1024-123X
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
Country of Publication:
United States
17 WIND ENERGY; 97 MATHEMATICS AND COMPUTING; Wind Energy; mathematics; mechanical equipment; signal processing; machine fault diagnosis; NREL