Skip to main content
Article thumbnail
Location of Repository

Feature group optimization for machinery fault diagnosis based on fuzzy measures

By Xiaofeng Liu, Lin Ma, Sheng Zhang and Joseph Mathew

Abstract

With the development of modern multi-sensor based data acquisition technology often used with advanced signal processing techniques, more and more features are being extracted for the purposes of fault diagnostics and prognostics of machinery integrity. Applying multiple features can enhance the condition monitoring capability and improve the fault diagnosis accuracy. However, an excessive number of features also increases the complexity of the data analysis task and often increases the time associated with the analysis process. A method of bringing some efficiency into this process is to choose the most sensitive feature subset instead. Fuzzy measures are helpful in this regard and have the ability to represent the importance and interactions among different criteria. Based on fuzzy measure theory, a feature selection approach for machinery fault diagnosis is presented in this paper. A heuristic least mean square algorithm is adopted to identify the fuzzy measures using training data set. Shapley values with respect to the fuzzy measures are applied as importance indexes to help choose the most sensitive features from a set of features. Interaction indexes with respect to the fuzzy measures are then employed to remove the redundant features. Vibration signals from a rolling element bearing test rig are used to validate the method. The results show that the proposed feature selection approach based on fuzzy measures is effective for fault diagnosis

Topics: 091300 MECHANICAL ENGINEERING, 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, Fault diagnosis, Feature selection, Fuzzy measures, Importance index, Interaction index
Publisher: Springer
Year: 2006
DOI identifier: 10.1007/978-1-84628-814-2_39
OAI identifier: oai:eprints.qut.edu.au:13301

Suggested articles

Citations

  1. (1993). A more efficient branch and bound algorithm for feature selection. Pattern Recognition,
  2. (1995). A new algorithm for identifying fuzzy measures and its application to pattern recognition.
  3. (1953). A value for n-person game. Contributions to the theory of games,
  4. (2003). An improved branch and bound algorithm for feature selection. Pattern Recognition Letters,
  5. (1999). and H.Yan, Color image segmentation using fuzzy integral and mountain clustering. Fuzzy Sets and Systems,
  6. (1997). Constructing fuzzy measures in expert systems. Fuzzy Sets and Systems,
  7. (2001). Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition,
  8. Diagnosis Principle and Application for the Fault Information. 2000: China Defence
  9. (2004). Face recognition using fuzzy integral and wavelet decomposition method.
  10. (2002). Feature selection toolbox software package. Pattern Recognition Letters,
  11. (2002). Feature selection toolbox. Pattern Recognition,
  12. (1996). Fusion of handwritten word classifiers. Pattern Recognition Letters,
  13. (2002). Generalized Choquet fuzzy integral fusion. Information Fusion,
  14. (1998). Human face image recognition: an evidence aggregation approach. Computer Vision and Image Understanding,
  15. Many are called, but few are chosen. Feature selection and error estimation in high dimensional spaces. Computer Methods and Programs
  16. (1993). Techniques for reading fuzzy measures (3): interaction index.
  17. (1996). The representation of importance and interaction of features by fuzzy measures. Pattern Recognition Letters,

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.