吉林大学学报(工学版) ›› 2004, Vol. ›› Issue (3): 491-495.

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Sensor fault diagnosis based on wavelet and neural network

LI Wenjun1, ZHANG Hongkun2, CHENG Xiusheng2   

  1. 1. College of Communications Engineering, Jilin University, Changchun 130022, China;
    2. College of Automotive Engineering, Jilin University, Changchun, 130022, China
  • Received:2004-01-05 Online:2004-07-01

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Abstract: A diagnosis method based on wavelet packet transform and BP neural network was proposed to detect and identify sensor abrupt fault. Since wavelet packet transform can accurately localize sensor signal characteristics both in time and frequency domain, it is very suitable for non-stationary signal analysis. After wavelet packet analysis for sensor output, eigenvector of energy changing rate was extracted, and classification of sensor fault was conducted by using BP neural network. The proposed method does not need construction of sensor model and measurement of sensor input. Hence redundant data can be reduced by omitting some wavelet packet coefficients and the capability of fault detection can be improved. Simulation results proved the effectiveness of this method.

Key words: artifical intellrgence, abrupt fault of sensors, wavelet packet transform, neural network, fault diagnosis

CLC Number: 

  • TP271
[1] JIN J, SHI J. Feature-prewrving data compression of stamping tonnage information using wavelets［J］. Technometrics,1999,41(4):327-339.
[2] AKANSU A N, HADDAD R A. Multiresolution Signal Decomposition:Transforms, Subbands, Wavelets［M］. New York:Academic, 1992.
[3] MALLAT S G.A theory for multiresolution signal decomposition:the wavelet representation［J］. IEEE Trans Pattern Anal Machine Intell, 1989,11:674-693.
[4] WICKERHAUSER M V. Adapted Wavelet Analysis from Theory to Software［M］. Natick, MA:Wellesley, 1994.
[5] LEES F P. Some data on the failure modes of instruments in the chemical plant environment［J］. Chem Eng,1973,277:418-421.
[6] ANYAKORA S N, LEES F P. Detection of instrument malfunction by process operator［J］. Chem Eng, 1972,264:304-309.
[7] LOPEZ J E, OLIVER K. Overview of wavelet/neural network fault diagnostic methods applied to rotating machinery［C］//Proc Joint Corf Technology Showcase Integrated Monitoring, Diagnostics and Failure Prevention, 1996:405-417.
[8] DONOVAN G C,GERONIMO J S,HARDIN D P,MASSOPUST P R. Construction of orthogonal wavelets using fractal interpolation functions［J］. SIAM Journal of Mathematical Analysis, 1996,27:1158-1192.
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