Skip to main content
padlock icon - secure page this page is secure

An intelligent model based on data mining and fuzzy logic for fault diagnosis of external gear hydraulic pumps

Buy Article:

$22.00 + tax (Refund Policy)

This paper presents a fault diagnosis method based on a fuzzy inference system (FIS) in combination with decision trees. Experiments were conducted on an external gear hydraulic pump. The vibration signal from a piezoelectric transducer is captured for the following conditions: Normal pump (GOOD), Journal-bearing with inner face wear (BIFW), Gear with tooth face wear (GTFW) and Journal-bearing with inner face wear and Gear with tooth face wear (G&BW), for three working levels of pump speed (1000, 1500 and 2000 r/min). The features of signal were extracted using descriptive statistic parameters. The J48 algorithm is used as a feature selection procedure to select pertinent features from the data set. The output of the J48 algorithm is a decision tree that was employed to produce the crisp if-then rule and membership function sets. The structure of the FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500 and 2000 r/min conditions were 100, 96.42 and 89.28, respectively. The results indicate that the combined J48-FIS model has the potential for fault diagnosis of hydraulic pumps.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: FIS; Intelligent fault diagnosis; J48 algorithm; hydraulic pump

Document Type: Research Article

Affiliations: Department of Agricultural Machinery Engineering, Faculty of Biosystems Engineering, University of Tehran, PO Box 4111, Karaj 31587-77871, Iran.

Publication date: November 1, 2009

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more