The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 52-638
Full Name: Ma Jin
Position: Doctor (Researcher)
Age: ON
Sex: Male
Address: North 3rd-Ring East Street No.15, Beijing
Country: CHINA
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E-mail address: majin30@yeah.net
Other E-mails: majin@bhxz.net
Title of the Paper: the principle of symptomatic granularity and its application in the reciprocating engine fault diagnosis
Authors as they appear in the Paper: Ma Jin Jiang Zhinong Gao Jinji
Email addresses of all the authors: majin30@yeah.net,jiangzhinong@263.net,gaojinji@263.net
Number of paper pages: 10
Abstract: On the basis of complicated fault feature of the reciprocating engine, a new feature reduction method based on the principle of the knowledge granularity to estimate the significance of symptomatic parameters is presented in this paper. The current problem that in the process of reducing and compressing the symptomatic parameters of fault diagnosis, the smallest symptom sets obtained is not always the smallest and optimal one, has been solved by the new method. By calculating on one instance of reciprocating engine knowledge set, the feature reduction method is effective.
Keywords: Symptomatic Parameter, Reciprocating Engine, Granularity Entropy, Fault Diagnosis, Fault Feature, Knowledge Granularity
EXTENSION of the file: .doc
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