Time series and Spectrum graph information
The uses an audio A/D for capturing the sensor signal. The number of samples acquired in this period is 4,096 points. A fast Fourier transform (FFT) algorithm is used to create a frequency spectrum. The frequency spectrum is displayed in the graph labeled Spectrum (see FIGURE F 1), which shows each of the frequencies and the voltage amplitude in mV RMS.
The Time Series graph is the acquired or sampled data in the time domain. The graph shows the combination of all the frequencies coming from the vibrating-wire sensor shortly after sensor excitation. The dominant frequency is the natural resonating frequency of the vibrating wire. The other frequencies can include noise pickup (from motors close to the sensor or pickup due to long wire lengths), harmonics of the natural frequency or harmonics of the noise (50/60 Hz harmonics), and/or mechanical obstruction (such as wire loosening or package movement that causes physical changes to wire vibration). The computes a signal-to-noise diagnostic by dividing the response amplitude by the noise amplitude.
The Time Series graph shows the decay from the start of the sampling to the end of the sampling. The decay is the dampening of the wire over time. The computes a decay ratio diagnostic from the time series ending amplitude divided by the beginning amplitude. Some sensors will decay very rapidly, others not. Characterizing the sensor decay and amplitude when the sensor is new is a good idea, so that over time, the health of the sensor can be monitored.
By changing the begin and end frequencies in the Options tab, the effects of narrowing can be of value for troubleshooting and solving problems with errant sensors or for improving the measurement. Ensure that when the begin and end frequencies are changes, the frequency range still captures the sensor signal.