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5 November 2005 Sensor fault diagnosis based on discrete wavelet transform and BP neural network
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Proceedings Volume 5998, Sensors for Harsh Environments II; 59980J (2005)
Event: Optics East 2005, 2005, Boston, MA, United States
Sensor technology is one of three major pillars of the modern information technology. With the extensive application of sensor, the dependability of the sensor is paid more and more attention. The development of sensor faults diagnose technology offers strong guarantee for using the sensor reliably. In this paper, the application of combining the wavelet and BP neural networks to sensors failure detection is studied, and a novel diagnosis method based on discrete wavelet transform and BP neural network was proposed to detect and identify sensor abrupt fault. Since wavelet transform can accurately localize sensor signal characteristics both in time and frequency domain, it is very suitable for non-stationary signal analysis. After discrete wavelet transform 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 coefficients and the capability of fault detection can be improved. Sensor fault diagnosis is simulated by the computer. Through a large amount of simulated examples it indicates that the sensors fault diagnosis method based on the theory of wavelet has characteristic such as good sensitivity, high accuracy rate and robust ability to overcome noise. Simulation results proved the effectiveness of this method.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Quan Liu and Xuemei Jiang "Sensor fault diagnosis based on discrete wavelet transform and BP neural network", Proc. SPIE 5998, Sensors for Harsh Environments II, 59980J (5 November 2005);

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