### Abstract

This paper deals with an application of wavelets for feature extraction and classification of machine faults. The statistical approach referred to as informative wavelet algorithm is utilized to generate wavelets and subsequent coefficients that are used as feature variables for the classification and diagnosis of machine faults. Informative wavelets are referred to classes of functions generated from elements of a dictionary of orthogonal bases, such as wavelet packet dictionary. Training data are used to construct probability distributions required for the computation of the entropy and mutual information. In our data analysis, we have used machine data acquired from a single cylinder engine under a series of induced faults in a test environment. The objective of the experiment was to evaluate the performance of the informative wavelet algorithm in classifying faults using real-world machine data and to examine the extent to which the results were influenced by different analyzing wavelets chosen for data analysis. The correlation structure of the informative wavelets as well as coefficient matrix are also examined.

Original language | English |
---|---|

Title of host publication | Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives |

Publisher | Springer-Verlag Berlin Heidelberg |

Pages | 183-192 |

Number of pages | 10 |

ISBN (Electronic) | 9783642344718 |

ISBN (Print) | 3642344704, 9783642344701 |

DOIs | |

Publication status | Published - 1 Aug 2013 |

### Fingerprint

### Keywords

- Fault diagnosis
- informative wavelets
- wavelet packet analysis

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives*(pp. 183-192). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34471-8_15

**Machine fault diagnosis using mutual information and informative wavelet.** / Tafreshi, Reza; Sassani, Farrokh; Ahmadi, Hossein; Dumont, Guy.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives.*Springer-Verlag Berlin Heidelberg, pp. 183-192. https://doi.org/10.1007/978-3-642-34471-8_15

}

TY - CHAP

T1 - Machine fault diagnosis using mutual information and informative wavelet

AU - Tafreshi, Reza

AU - Sassani, Farrokh

AU - Ahmadi, Hossein

AU - Dumont, Guy

PY - 2013/8/1

Y1 - 2013/8/1

N2 - This paper deals with an application of wavelets for feature extraction and classification of machine faults. The statistical approach referred to as informative wavelet algorithm is utilized to generate wavelets and subsequent coefficients that are used as feature variables for the classification and diagnosis of machine faults. Informative wavelets are referred to classes of functions generated from elements of a dictionary of orthogonal bases, such as wavelet packet dictionary. Training data are used to construct probability distributions required for the computation of the entropy and mutual information. In our data analysis, we have used machine data acquired from a single cylinder engine under a series of induced faults in a test environment. The objective of the experiment was to evaluate the performance of the informative wavelet algorithm in classifying faults using real-world machine data and to examine the extent to which the results were influenced by different analyzing wavelets chosen for data analysis. The correlation structure of the informative wavelets as well as coefficient matrix are also examined.

AB - This paper deals with an application of wavelets for feature extraction and classification of machine faults. The statistical approach referred to as informative wavelet algorithm is utilized to generate wavelets and subsequent coefficients that are used as feature variables for the classification and diagnosis of machine faults. Informative wavelets are referred to classes of functions generated from elements of a dictionary of orthogonal bases, such as wavelet packet dictionary. Training data are used to construct probability distributions required for the computation of the entropy and mutual information. In our data analysis, we have used machine data acquired from a single cylinder engine under a series of induced faults in a test environment. The objective of the experiment was to evaluate the performance of the informative wavelet algorithm in classifying faults using real-world machine data and to examine the extent to which the results were influenced by different analyzing wavelets chosen for data analysis. The correlation structure of the informative wavelets as well as coefficient matrix are also examined.

KW - Fault diagnosis

KW - informative wavelets

KW - wavelet packet analysis

UR - http://www.scopus.com/inward/record.url?scp=84929526841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929526841&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-34471-8_15

DO - 10.1007/978-3-642-34471-8_15

M3 - Chapter

AN - SCOPUS:84929526841

SN - 3642344704

SN - 9783642344701

SP - 183

EP - 192

BT - Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives

PB - Springer-Verlag Berlin Heidelberg

ER -