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《Chinese Journal of Sensors and Actuators》 2018-06
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Research on Signal Noise Reduction of Capacitive Grid Sensor Based on Wavelet-EEMD-Adaline Network

DENG Huifang;SUN Chuanmeng;YANG Meng;XIE Rui;WEI Yafeng;Key Laboratory Of Instrumentation Science and Dynamic Measurement(North University of China) ,Ministry Of Education;Nanjing University of Posts and Telecommunications,College of Automation;  
The capacitance of torsional vibration signal of rotating shaft detected by capacitive grid sensor is disturbed by environment noise and electromagnetic noise,which make the signal-to-noise ratio low and the faint signal hard to be extracted.This paper presents a wavelet-EEMD-Adaline adaptive linear neural network denoising method.The proposed method carries out wavelet denoising,EEMD denoising and Adaline denoising on the signal,using three-level denoising,noise filtering and cancellation to approximate the original signal. The effectiveness of this method is validated by using typical noise-added ultrasonic signals,Doppler signals and Block signals,compared with the denoising effect of EEMD denoising method,EEMD based on wavelet decomposition denoising method. The results of the verification experiment show that while the SNR of signal de-noising in the latter two methods is small( both less than 20),the SNR( RMSE) of the proposed method,which SNR is improved by 90 and RMSE decreases from 1.038 5 to 0.009 5 for Doppler( 9 d B) signal,increases( decreases) significantly. At last,the denoising results of weak signal with large noise measured by capacitive sensor circuit show that this method has better denoising performance,and the signal is smooth and stable after denoising.
【Fund】: 车辆行驶工况下传动系扭振特征参数提取及特性分析项目(201701D221122)
【CateGory Index】: TP212
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