Wavelet analysis of sensor data for qualitative features extraction
Wavelet analysis of sensor data for qualitative features extraction
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DOI：
10.1117/12.602580
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发表时间：
2005-03
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影响因子：
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通讯作者：
A. M. Amini
中科院分区：
文献类型：
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作者：
A. M. Amini
传感器和系统的健康状况通过从传感器收集的信息进行监测。建立了一种正常的操作模式。任何偏离正常行为的情况都表明发生了变化。使用一个RC网络对主要过程进行建模，该过程由升压（充电）、漂移和降压（放电）来定义。在系统处于漂移状态时添加传感器干扰和尖峰。每当一个过程特征出现（例如阶跃变化）时，系统运行的时间至少为主要过程的三个时间常数。为了提取时间信息和形状分离，使用了小波变换。使用连续小波变换和离散小波变换对结果进行分析。结果表明每个过程都对应着不同的形状。将小波变换结果与使用汉明窗和傅里叶变换得到的信号平均功率进行比较。对信号进行傅里叶变换分析时，通过选择信号的每个点以及之前收集的尾随数据窗口。选择了两个尾随窗口长度；一个等于主要过程的两个时间常数，另一个等于传感器干扰的两个时间常数。接下来，从每组数据中去除直流分量，然后将数据通过一个窗口，接着计算每组的频谱。为了提取特征，将信号功率、峰值和频谱面积相对于时间进行绘图。
The health of a sensor and system is monitored by information gathered from the sensor. A normal mode of operation is established. Any deviation from the normal behavior indicates a change. An RC network is used to model the main process, which is defined by a step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system runs for a period of at least three time-constants of the main process every time a process feature occurs (e.g. step change). To extract time information and shape isolation the Wavelet Transform is used. The results are analyzed using continuous as well as discrete wavelet transforms. The results indicate distinct shapes corresponding to each process. The Wavelet Transform results are compared to the signal average power using hamming window and Fourier Transform. The Fourier Transform analysis of the signal is carried out by selecting each point of the signal with a window of trailing data collected previously. Two trailing window lengths are selected; one equal to two time constant of the main process and the other equal to two time constant of the sensor disturbance. Next, the DC is removed from each set of data and then the data are passed through a window followed by calculation of spectra for each set. In order to extract features, the signal power, peak, and spectral area are plotted vs. time