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首页> 外文会议>ICA;Conference on independent component analyses, wavelets, neural networks, biosystems, and nanoengineering VII; 20090413-17;20090413-17; Orlando, FL(US);Orlando, FL(US) >Sensor performance monitoring using Fourier and Wavelet Transforms
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Sensor performance monitoring using Fourier and Wavelet Transforms

机译：使用傅立叶和小波变换监控传感器性能

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摘要

In this paper, we simulate sensor behavior by synthesizing different features of a sensor output under normal mode of operation. Any deviation from the normal behavior indicates a change. The synthesized features indicate behavior of different physical processes that a sensor is monitoring. The shapes of these features must be extracted from sensor output for sensor health management. The extracted features are compared to features of healthy sensor to monitor its performance. In this paper, we compare Fourier and Wavelet Transform methods for extraction of the sensor output features. The wavelet Transform Analysis is performed on the simulated data described above with Poisson distributed noise. The simulated data with Poisson distributed noise of SNRs ranging from 10 to 500 are generated. The data are analyzed using Discrete as well as Discretized Continuous Wavelet Transforms. The Short-Time Fourier Transform of the Signal is taken using the Hamming window. Three window widths are used. The DC value is removed from the windowed data prior to taking the FFT. The resulting three dimensional spectral plots provide good time frequency resolution. The results indicate distinct shapes corresponding to each process.
机译：在本文中，我们通过在正常操作模式下综合传感器输出的不同功能来模拟传感器行为。与正常行为的任何偏离都表明发生了变化。合成的特征指示传感器正在监视的不同物理过程的行为。这些特征的形状必须从传感器输出中提取出来以进行传感器健康管理。将提取的特征与健康传感器的特征进行比较以监视其性能。在本文中，我们比较了傅里叶和小波变换方法来提取传感器输出特征。对具有Poisson分布噪声的上述模拟数据执行小波变换分析。生成具有SNR范围为10到500的Poisson分布噪声的模拟数据。使用离散以及离散连续小波变换来分析数据。使用汉明窗口获取信号的短时傅立叶变换。使用三个窗口宽度。在进行FFT之前，从窗口数据中删除DC值。所得的三维频谱图可提供良好的时频分辨率。结果表明对应于每个过程的不同形状。

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